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null | https://openreview.net/forum?id=us4qvNWeGB | @inproceedings{
kang2023advancing,
title={Advancing Graph Neural Networks Through Joint Time-Space Dynamics},
author={Qiyu Kang and Yanan Zhao and Kai Zhao and Xuhao Li and Qinxu Ding and Wee Peng Tay and Sijie Wang},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=us4qvNWeGB}
} | We introduce the GeneRAlized Fractional Time-space graph diffusion network (GRAFT), a framework combining temporal and spatial nonlocal operators on graphs to effectively capture long-range interactions across time and space. Leveraging time-fractional diffusion processes, GRAFT encompasses a system's full historical context, while the $d$-path Laplacian diffusion ensures extended spatial interactions based on shortest paths. Notably, GRAFT mitigates the over-squashing problem common in graph networks. Empirical results show its prowess on self-similar, tree-like data due to its fractal-conscious design with fractional time derivatives. We delve deeply into the mechanics of GRAFT, emphasizing its distinctive ability to encompass both time and space diffusion processes through a random walk perspective. | Advancing Graph Neural Networks Through Joint Time-Space Dynamics | [
"Qiyu Kang",
"Yanan Zhao",
"Kai Zhao",
"Xuhao Li",
"Qinxu Ding",
"Wee Peng Tay",
"Sijie Wang"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=sa7WBZgjrX | @inproceedings{
flores2023twostep,
title={Two-Step Bayesian {PINN}s for Uncertainty Estimation},
author={Pablo Flores and Olga Graf and Pavlos Protopapas and Karim Pichara},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=sa7WBZgjrX}
} | We use a two-step procedure to train Bayesian neural networks that provide uncertainties over the solutions to differential equation (DE) systems provided by Physics-Informed Neural Networks (PINNs). We take advantage of available error bounds over PINNs to formulate a heteroscedastic variance that improves the uncertainty estimation. Furthermore, we solve forward problems and utilize the uncertainties obtained to improve parameter estimation in inverse problems in the fields of cosmology and fermentation. | Two-Step Bayesian PINNs for Uncertainty Estimation | [
"Pablo Flores",
"Olga Graf",
"Pavlos Protopapas",
"Karim Pichara"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=rnhkE2vb4r | @inproceedings{
das2023odesolvers,
title={\${ODE}\$Solvers are also Wayfinders: Neural {ODE}s for Multi-Agent Pathplanning},
author={Progyan Das and Anirban Dasgupta and Dwip Dalal},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=rnhkE2vb4r}
} | Multi-agent path planning is a central challenge in areas such as robotics, autonomous vehicles, and swarm intelligence. Traditional discrete methods often struggle with real-time adaptability and computational efficiency, emphasizing the need for continuous, optimizable solutions. This paper introduces a novel approach that harnesses Neural Ordinary Differential Equations (Neural ODEs) for multi-agent path planning in a continuous-time framework. By parameterizing agent dynamics using neural networks within these ODEs, we enable end-to-end trajectory optimization. The inherent dynamics of ODEs facilitate collision avoidance. We demonstrate our method's effectiveness across both 2D and 3D scenarios, navigating multiple agents amidst obstacles, underscoring the potential of Neural ODEs to transform path planning. | ODESolvers are also Wayfinders: Neural ODEs for Multi-Agent Pathplanning | [
"Progyan Das",
"Dwip Dalal",
"Anirban Dasgupta"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=qd7q9lB5hY | @inproceedings{
white2023physicsinformed,
title={Physics-Informed Neural Operators with Exact Differentiation on Arbitrary Geometries},
author={Colin White and Julius Berner and Jean Kossaifi and Mogab Elleithy and David Pitt and Daniel Leibovici and Zongyi Li and Kamyar Azizzadenesheli and Anima Anandkumar},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=qd7q9lB5hY}
} | Neural Operators can learn operators from data, for example, to solve partial differential equations (PDEs). In some cases, this data-driven approach is not sufficient, e.g., if the data is limited, or only available at a resolution that does not permit resolving the underlying physics. The Physics-Informed Neural Operator (PINO) aims to solve this issue by adding the PDE residual as a loss to the Fourier Neural Operator (FNO). Several methods have been proposed to compute the derivatives appearing in the PDE, such as finite differences and Fourier differentiation. However, these methods are limited to regular grids and suffer from inaccuracies. In this work, we propose the first method capable of exact derivative computations for general functions on arbitrary geometries. We leverage the Geometry Informed Neural Operator (GINO), a recently proposed graph-based extension of FNO. While GINO can be queried at arbitrary points in the output domain, it is not differentiable with respect to those points due to a discrete neighbor search procedure. We introduce a fully differentiable extension of GINO that uses a differentiable weight function and neighbor caching in order to maintain the efficiency of GINO while allowing for exact derivatives. We empirically show that our method matches prior PINO methods while being the first to compute exact derivatives for arbitrary query points. | Physics-Informed Neural Operators with Exact Differentiation on Arbitrary Geometries | [
"Colin White",
"Julius Berner",
"Jean Kossaifi",
"Mogab Elleithy",
"David Pitt",
"Daniel Leibovici",
"Zongyi Li",
"Kamyar Azizzadenesheli",
"Anima Anandkumar"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=nl1ZzdHpab | @inproceedings{
bafghi2023pinnstorch,
title={{PINN}s-Torch: Enhancing Speed and Usability of Physics-Informed Neural Networks with PyTorch},
author={Reza Akbarian Bafghi and Maziar Raissi},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=nl1ZzdHpab}
} | Physics-informed neural networks (PINNs) stand out for their ability in supervised learning tasks that align with physical laws, especially nonlinear partial differential equations (PDEs). In this paper, we introduce "PINNs-Torch", a Python package that accelerates PINNs implementation using the PyTorch framework and streamlines user interaction by abstracting PDE issues. While we utilize PyTorch's dynamic computational graph for its flexibility, we mitigate its computational overhead in PINNs by compiling it to static computational graphs. In our assessment across 8 diverse examples, covering continuous, discrete, forward, and inverse configurations, naive PyTorch is slower than TensorFlow; however, when integrated with CUDA Graph and JIT compilers, training speeds can increase by up to 9 times relative to TensorFlow implementations. Additionally, through a real-world example, we highlight situations where our package might not deliver speed improvements. For community collaboration and future developments, our package code is accessible at: https://github.com/rezaakb/pinns-torch. | PINNs-Torch: Enhancing Speed and Usability of Physics-Informed Neural Networks with PyTorch | [
"Reza Akbarian Bafghi",
"Maziar Raissi"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=mcIbqPMKCU | @inproceedings{
lei2023oneshot,
title={One-Shot Transfer Learning for Nonlinear {ODE}s},
author={Wanzhou Lei and Pavlos Protopapas and Joy Parikh},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=mcIbqPMKCU}
} | We introduce a generalizable approach that combines perturbation method and one-shot transfer learning to solve nonlinear ODEs with a single polynomial term, using Physics-Informed Neural Networks (PINNs). Our method transforms non-linear ODEs into linear ODE systems, trains a PINN across varied conditions, and offers a closed-form solution for new instances within the same non-linear ODE class. We demonstrate the effectiveness of this approach on the Duffing equation and suggest its applicability to similarly structured PDEs and ODE systems. | One-Shot Transfer Learning for Nonlinear ODEs | [
"Wanzhou Lei",
"Pavlos Protopapas",
"Joy Parikh"
] | Workshop/DLDE | 2311.14931 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=kZmxbQ0l3l | @inproceedings{
chatain2023deep,
title={Deep {PDE} Solvers for Subgrid Modelling and Out-of-Distribution Generalization},
author={Patrick Chatain and Adam M Oberman},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=kZmxbQ0l3l}
} | Climate and weather modelling (CWM) is an important area where ML models are used for subgrid modelling: making predictions of processes occurring at scales too small to be resolved by standard solution methods (Brasseur & Jacob, 2017). These models are expected to make accurate predictions, even on out-of-distribution (OOD) data, and are additionally expected to respect important physical constraints of the ground truth model (Kashinath et al., 2021). While many specialized ML PDE solvers have been developed, the particular requirements of CWM models have not been addressed so far. The goal of this work is to address them. We propose and develop a novel architecture, which matches or exceeds the performance of standard ML models, and which demonstrably succeeds in OOD generalization. The architecture is based on expert knowledge of the structure of PDE solution operators, which permits the model to also obey important physical constraints. | Deep PDE Solvers for Subgrid Modelling and Out-of-Distribution Generalization | [
"Patrick Chatain",
"Adam M Oberman"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ihbEbWpaaF | @inproceedings{
ripken2023multiscale,
title={Multiscale Neural Operators for Solving Time-Independent {PDE}s},
author={Winfried Ripken and Lisa Coiffard and Felix Pieper and Sebastian Dziadzio},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=ihbEbWpaaF}
} | Time-independent Partial Differential Equations (PDEs) on large meshes pose significant challenges for data-driven neural PDE solvers. We introduce a novel graph rewiring technique to tackle some of these challenges, such as aggregating information across scales and on irregular meshes. Our proposed approach bridges distant nodes, enhancing the global interaction capabilities of GNNs. Our benchmarks on three datasets reveal that GNN-based methods set new performance standards for time-independent PDEs on irregular meshes. Finally, we show that our graph rewiring strategy boosts the performance of baseline methods, achieving state-of-the-art results in one of the tasks. | Multiscale Neural Operators for Solving Time-Independent PDEs | [
"Winfried Ripken",
"Lisa Coiffard",
"Felix Pieper",
"Sebastian Dziadzio"
] | Workshop/DLDE | 2311.05964 | [
"https://github.com/merantix-momentum/multiscale-pde-operators"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=hiPJp5PhaX | @inproceedings{
belogolovsky2023individualized,
title={Individualized Dosing Dynamics via Neural Eigen Decomposition},
author={Stav Belogolovsky and Ido Greenberg and Danny Eytan and Shie Mannor},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=hiPJp5PhaX}
} | Dosing models often use differential equations to model biological dynamics.
Neural differential equations in particular can learn to predict the derivative of a process, which permits predictions at irregular points of time.
However, this temporal flexibility often comes with a high sensitivity to noise, whereas medical problems often present high noise and limited data.
Moreover, medical dosing models must generalize reliably over individual patients and changing treatment policies.
To address these challenges, we introduce the Neural Eigen Stochastic Differential Equation algorithm (NESDE).
NESDE provides individualized modeling (using patient-level parameters); generalization to new treatment policies (using decoupled control); tunable expressiveness according to the noise level (using piecewise linearity); and fast, continuous, closed-form prediction (using spectral representation).
We demonstrate the robustness of NESDE in real medical problems, and use the learned dynamics to publish simulated medical gym environments. | Individualized Dosing Dynamics via Neural Eigen Decomposition | [
"Stav Belogolovsky",
"Ido Greenberg",
"Danny Eytan",
"Shie Mannor"
] | Workshop/DLDE | 2306.14020 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fUrEuIiF9B | @inproceedings{
tan2023neural,
title={Neural Differential Recurrent Neural Network with Adaptive Time Steps},
author={Yixuan Tan and Liyan Xie and Xiuyuan Cheng},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=fUrEuIiF9B}
} | The neural Ordinary Differential Equation (ODE) model has shown success in learning
continuous-time processes from observations on discrete time stamps. In this work, we consider the modeling and forecasting of time series data that are non-stationary and may have sharp changes like spikes. We propose an RNN-based model, called $\textit{RNN-ODE-Adap}$, that uses a neural ODE to represent the time development of the hidden states, and we adaptively select time steps based on the steepness of changes of the data over time so as to train the model more efficiently for the ''spike-like'' time series. Theoretically, $\textit{RNN-ODE-Adap}$ yields provably a consistent estimation of the intensity function for the Hawkes-type time series data. We also provide an approximation analysis of the RNN-ODE model showing the benefit of adaptive steps. The proposed model is demonstrated to achieve higher prediction accuracy with reduced computational cost on simulated dynamic system data and point process data and on a real electrocardiography dataset. | Neural Differential Recurrent Neural Network with Adaptive Time Steps | [
"Yixuan Tan",
"Liyan Xie",
"Xiuyuan Cheng"
] | Workshop/DLDE | 2306.01674 | [
"https://github.com/yixuan-tan/rnn_ode_adap"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=eu0Bhwg8hy | @inproceedings{
dongre2023evaluating,
title={Evaluating Uncertainty Quantification approaches for Neural {PDE}s in scientific application},
author={Vardhan Dongre and Gurpreet Singh Hora},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=eu0Bhwg8hy}
} | The accessibility of spatially distributed data, enabled by affordable sensors, field, and numerical experiments, has facilitated the development of data-driven solutions for scientific problems, including climate change, weather prediction, and urban planning. Neural Partial Differential Equations (Neural PDEs), which combine deep learning (DL) techniques with domain expertise (e.g., governing equations) for parameterization, have proven to be effective in capturing valuable correlations within spatiotemporal datasets. However, sparse and noisy measurements coupled with modeling approximation introduce aleatoric and epistemic uncertainties. Therefore, quantifying uncertainties propagated from model inputs to outputs remains a challenge and an essential goal for establishing the trustworthiness of Neural PDEs. This work evaluates various Uncertainty Quantification (UQ) approaches for both Forward and Inverse Problems in scientific applications. Specifically, we investigate the effectiveness of Bayesian methods, such as Hamiltonian Monte Carlo (HMC) and Monte-Carlo Dropout (MCD), and a more conventional approach, Deep Ensembles (DE). To illustrate their performance, we take two canonical PDEs: Burger's equation and the Navier-Stokes equation. Our results indicate that Neural PDEs can effectively reconstruct flow systems and predict the associated unknown parameters. However, it is noteworthy that the results derived from Bayesian methods, based on our observations, tend to display a higher degree of certainty in their predictions as compared to those obtained using the DE. This elevated certainty in predictions suggests that Bayesian techniques might underestimate the true underlying uncertainty, thereby appearing more confident in their predictions than the DE approach. | Evaluating Uncertainty Quantification approaches for Neural PDEs in scientific application | [
"Vardhan Dongre",
"Gurpreet Singh Hora"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=eOODNEuD7D | @inproceedings{
josias2023multimodal,
title={Multimodal base distributions for continuous-time normalising flows},
author={Shane Josias and Willie Brink},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=eOODNEuD7D}
} | We investigate the utility of a multimodal base distribution in continuous-time normalising flows. Multimodality is incorporated through a Gaussian mixture model (GMM) centred at the empirical means of a target distribution's modes. In- and out-of-distribution likelihoods are reported for flows trained with a unimodal and multimodal base distribution. Our results show that the GMM base distribution leads to performance that is comparable to a standard (unimodal) Gaussian distribution for in-distribution likelihoods, but provides the ability to sample from a specific mode in the target distribution, yields generated samples of improved quality, and gives more reliable out-of-distribution likelihoods for low-dimensional input spaces. We conclude that a GMM base distribution is an attractive alternative to the standard base, whose inclusion incurs little to no cost and whose parameterisation may assist with more reliable out-of-distribution likelihoods. | Multimodal base distributions for continuous-time normalising flows | [
"Shane Josias",
"Willie Brink"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=eGH9uUqD9P | @inproceedings{
asokan2023elegant,
title={{EL}e{GAN}t: An Euler-Lagrange Analysis of Wasserstein Generative Adversarial Networks},
author={Siddarth Asokan and Chandra Sekhar Seelamantula},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=eGH9uUqD9P}
} | We consider Wasserstein generative adversarial networks (WGAN) with a gradient-norm penalty and analyze the underlying functional optimization problem within a variational setting. The optimal discriminator in this setting is the solution to a Poisson differential equation, and can be obtained in closed form without having to train a neural network. We illustrate this by employing a Fourier-series approximation to solve the Poisson differential equation. Experimental results based on synthesized low-dimensional Gaussian data demonstrate superior convergence behavior of the proposed approach in comparison with the baseline WGAN variants that employ weight-clipping, gradient or Lipschitz penalties on the discriminator. Further, within this setting, the optimal Lagrange multiplier can be computed in closed-form, and serves as a proxy for measuring GAN generator convergence. This work is an extended abstract, summarizing Asokan & Seelamantula (2023). | ELeGANt: An Euler-Lagrange Analysis of Wasserstein Generative Adversarial Networks | [
"Siddarth Asokan",
"Chandra Sekhar Seelamantula"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=RFFTb7DIqi | @inproceedings{
abrevaya2023effective,
title={Effective Latent Differential Equation Models via Attention and Multiple Shooting},
author={Germ{\'a}n Abrevaya and Mahta Ramezanian-Panahi and Jean-Christophe Gagnon-Audet and Pablo Polosecki and Irina Rish and Silvina Ponce Dawson and Guillermo Cecchi and Guillaume Dumas},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=RFFTb7DIqi}
} | The GOKU-net is a continuous-time generative model that allows leveraging prior knowledge in the form of differential equations. We present GOKU-UI, an evolution of the GOKU-nets, which integrates attention mechanisms and a novel multiple shooting training strategy in the latent space. On simulated data, GOKU-UI significantly improves performance in reconstruction and forecasting, outperforming baselines even with 16 times less training data. Applied to empirical human brain data, using stochastic Stuart-Landau oscillators, it is able to effectively capture complex brain dynamics, surpassing baselines in reconstruction and better predicting future brain activity up to 15 seconds ahead. Ultimately, our research provides further evidence on the fruitful symbiosis given by the combination of established scientific insights and modern machine learning. | Effective Latent Differential Equation Models via Attention and Multiple Shooting | [
"Germán Abrevaya",
"Mahta Ramezanian-Panahi",
"Jean-Christophe Gagnon-Audet",
"Pablo Polosecki",
"Irina Rish",
"Silvina Ponce Dawson",
"Guillermo Cecchi",
"Guillaume Dumas"
] | Workshop/DLDE | 2307.05735 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=Lhm15nDeGH | @inproceedings{
majumdar2023can,
title={Can Physics informed Neural Operators self improve?},
author={Ritam Majumdar and Amey Varhade and Shirish Karande and Lovekesh Vig},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=Lhm15nDeGH}
} | Self-training techniques have shown remarkable value across many deep learning models and tasks. However, such techniques remain largely unexplored when considered in the context of learning fast solvers for systems of partial differential equations (Eg: Neural Operators). In this work, we explore the use of self-training for Fourier Neural Operators (FNO). Neural Operators emerged as a data driven technique, however, data from experiments or traditional solvers is not always readily available. Physics Informed Neural Operators (PINO) overcome this constraint by utilizing a physics loss for the training, however the accuracy of PINO trained without data does not match the performance obtained by training with data. In this work we show that self-training can be used to close this gap in performance. We examine canonical examples, namely the 1D-Burgers and 2D-Darcy PDEs, to showcase the efficacy of self-training. Specifically, FNOs, when trained exclusively with physics loss through self-training, approach $1.07\times$ for Burgers and $1.02\times$ for Darcy, compared to FNOs trained with both data and physics loss. Furthermore, we discover that pseudo-labels can be used for self-training without necessarily training to convergence in each iteration. A consequence of this is that we are able to discover self-training schedules that improve upon the baseline performance of PINO in terms of accuracy as well as time. | Can Physics informed Neural Operators Self Improve? | [
"Ritam Majumdar",
"Amey Varhade",
"Shirish Karande",
"Lovekesh Vig"
] | Workshop/DLDE | 2311.13885 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=IUowDkxcci | @inproceedings{
xue2023vertical,
title={Vertical {AI}-driven Scientific Discovery},
author={Yexiang Xue},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=IUowDkxcci}
} | Automating scientific discovery has been a grand goal of Artificial Intelligence (AI) and will bring tremendous societal impact if it succeeds. Despite exciting progress, most endeavor in learning scientific equations from experiment data focuses on the horizontal discovery paths, i.e., they directly search for the best equation in the full hypothesis space. Horizontal paths are challenging because of the associated exponentially large search space. Our work explores an alternative vertical path, which builds scientific equations in an incremental way, starting from one that models data in control variable experiments in which most variables are held as constants. It then extends expressions learned in previous generations via adding new independent variables, using new control variable experiments in which these variables are allowed to vary. This vertical path was motivated by human scientific discovery processes. Experimentally, we demonstrate that such vertical discovery paths expedite symbolic regression. It also improves learning physics models describing nano-structure evolution in computational materials science. | Vertical AI-driven Scientific Discovery | [
"Yexiang Xue"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=I95YA5Vyjd | @inproceedings{
haxholli2023enhanced,
title={Enhanced Distribution Modelling via Augmented Architectures For Neural {ODE} Flows},
author={Etrit Haxholli and Marco Lorenzi},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=I95YA5Vyjd}
} | While the neural ODE formulation of normalizing flows such as in FFJORD enables us to calculate the determinants of free form Jacobians in $\mathcal{O}(D)$ time, the flexibility of the transformation underlying neural ODEs has been shown to be suboptimal. In this paper, we present AFFJORD, a neural ODE-based normalizing flow which enhances the representation power of FFJORD by defining the neural ODE through special augmented transformation dynamics which preserve the topology of the space. Furthermore, we derive the Jacobian determinant of the general augmented form by generalizing the chain rule in the continuous sense into the $\textit{cable rule}$, which expresses the forward sensitivity of ODEs with respect to their initial conditions. The cable rule gives an explicit expression for the Jacobian of a neural ODE transformation, and provides an elegant proof of the instantaneous change of variable. Our experimental results on density estimation in synthetic and high dimensional data, such as MNIST, CIFAR-10 and CelebA ($32\times32$), show that AFFJORD outperforms the baseline FFJORD through the improved flexibility of the underlying vector field. | Enhanced Distribution Modelling via Augmented Architectures For Neural ODE Flows | [
"Etrit Haxholli",
"Marco Lorenzi"
] | Workshop/DLDE | 2306.02731 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=FsJ2FdQ19C | @inproceedings{
liu2023does,
title={Does In-Context Operator Learning Generalize to Domain-Shifted Settings?},
author={Jerry Weihong Liu and N. Benjamin Erichson and Kush Bhatia and Michael W. Mahoney and Christopher Re},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=FsJ2FdQ19C}
} | Neural network-based approaches for learning differential equations (DEs) have demonstrated generalization capabilities within a DE solution or operator instance. However, because standard techniques can only represent the solution function or operator for a single system at a time, the broader notion of generalization across classes of DEs has so far gone unexplored. In this work, we investigate whether commonalities across DE classes can be leveraged to transfer knowledge about solving one DE towards solving another --- without updating any model parameters. To this end, we leverage the recently-proposed in-context operator learning (ICOL) framework, which trains a model to identify in-distribution operators given a small number of input-output pairs as examples. Our implementation is motivated by pseudospectral methods, a class of numerical solvers that can be systematically applied to a range of DEs. For a natural distribution of 1D linear ordinary differential equations (ODEs), we identify a connection between operator learning and in-context linear regression. Applying recent results demonstrating the capabilities of Transformers to in-context learn linear functions, our reduction to least squares helps to explain why Transformers can be expected to solve ODEs in-context. Empirically, we demonstrate that ICOL is robust to a range of distribution shifts, including observational noise, domain-shifted inputs, varying boundary conditions, and surprisingly, even operators from functional forms unseen during training. | Does In-Context Operator Learning Generalize to Domain-Shifted Settings? | [
"Jerry Weihong Liu",
"N. Benjamin Erichson",
"Kush Bhatia",
"Michael W. Mahoney",
"Christopher Re"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=9hcsB4oYxG | @inproceedings{
demeule2023adaptive,
title={Adaptive Resolution Residual Networks},
author={L{\'e}a Demeule and Mahtab Sandhu and Glen Berseth},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=9hcsB4oYxG}
} | We introduce Adaptive Resolution Residual Networks (ARRNs), a form of neural operator that enables the creation of networks for signal-based tasks that can be rediscretized to suit any signal resolution. ARRNs are composed of a chain of Laplacian residuals that each contain ordinary layers, which do not need to be rediscretizable for the whole network to be rediscretizable. ARRNs have the property of requiring a lower number of Laplacian residuals for exact evaluation on lower-resolution signals, which greatly reduces computational cost. ARRNs also implement Laplacian dropout, which encourages networks to become robust to low-bandwidth signals. ARRNs can thus be trained once at high-resolution and then be rediscretized on the fly at a suitable resolution with great robustness. | Adaptive Resolution Residual Networks | [
"Léa Demeule",
"Mahtab Sandhu",
"Glen Berseth"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=8AYetbupoZ | @inproceedings{
huang2023tango,
title={{TANGO}: Time-reversal Latent Graph{ODE} for Multi-Agent Dynamical Systems},
author={Zijie Huang and Wanjia Zhao and Jingdong Gao and Ziniu Hu and Xiao Luo and Yadi Cao and Yuanzhou Chen and Yizhou Sun and Wei Wang},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=8AYetbupoZ}
} | Learning complex multi-agent system dynamics from data is crucial across many domains like physical simulations and material modeling. Existing physics-informed approaches, like Hamiltonian Neural Network, introduce inductive bias by strictly following energy conservation law. However, many real-world systems do not strictly conserve energy. Thus, we focus on Time-Reversal Symmetry, a broader physical principle indicating that system dynamics should remain invariant when time is reversed. This principle not only preserves energy in conservative systems but also serves as a strong inductive bias for non-conservative, reversible systems.
In this paper, we propose a simple-yet-effective self-supervised regularization term as a soft constraint that aligns the forward and backward trajectories predicted by a continuous graph neural network-based ordinary differential equation (GraphODE).
In addition, we theoretically show that our regularization essentially minimizes higher-order Taylor expansion terms during the ODE integration steps, which enables our model to be more noise-tolerant and even applicable to irreversible systems. | TANGO: Time-reversal Latent GraphODE for Multi-Agent Dynamical Systems | [
"Zijie Huang",
"Wanjia Zhao",
"Jingdong Gao",
"Ziniu Hu",
"Xiao Luo",
"Yadi Cao",
"Yuanzhou Chen",
"Yizhou Sun",
"Wei Wang"
] | Workshop/DLDE | 2310.06427 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=6sJqg7xwio | @inproceedings{
lobashev2023on,
title={On the Generalization of Deep Neural Networks for Optimal Sensor Placement in Global Ocean Forecasting},
author={Alexander Lobashev and Nikita Turko and Konstantin Ushakov and Maxim Kaurkin and Rashit Ibrayev},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=6sJqg7xwio}
} | The focus of this study is on the generalization of neural networks, particularly in the context of sensor placement for global climate models' forecasts. The goal is to determine if sensor placement strategies derived through training a deep learning model, which is tasked with reconstructing a physical field from a set of measurements, can be effectively applied to a real high-resolution ocean global circulation model. The research compares different sensor placement methods, including one achieved using the Concrete Autoencoder method. Through modeling under varied initial conditions of the World Ocean state, it was found that sensor placements informed by deep learning methods outperformed others in forecast accuracy when using a comparable number of sensors. This finding underscores the potential of deep learning-informed sensor placement as a powerful tool for refining the predictive capabilities of global climate models and accelerating the data assimilation system without extensive revisions to their source code. | On the Generalization of Deep Neural Networks for Optimal Sensor Placement in Global Ocean Forecasting | [
"Alexander Lobashev",
"Nikita Turko",
"Konstantin Ushakov",
"Maxim Kaurkin",
"Rashit Ibrayev"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3FBY4NdDVU | @inproceedings{
disch2023a,
title={A Holistic Vision: Modeling Patient Trajectories in Longitudinal Medical Imaging},
author={Nico Disch and David Zimmerer},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=3FBY4NdDVU}
} | In medical data analysis, human practitioners have long excelled at adopting a holistic approach, considering a wide range of patient information, including multiple imaging sources and evolving medical histories. A prime example can be found in tumor boards, where, among others, radiologists evaluate a multitude of images while taking into account dynamic patient narratives.
However, within the domain of medical image analysis, the current focus often narrows down to individual images, or if longitudinal data is used, the task is to infer non-dense predictions, like classification.
However, if we can leverage multiple time points and model a patient trajectory, we can predict patient status on an image level at an arbitrary time point in the future.
It could not only lead to better predictions for the current time point, i.e. for image segmentation, but it can also lead to a more holistic approach in medical image analysis, more akin to the human approach.
In response to this disparity, our work, we motivate the need for longitudinal medical image analysis and we present a model that can deal with sparse and irregular longitudinal series, and without sacrificing generality, generate images. | A Holistic Vision: Modeling Patient Trajectories in Longitudinal Medical Imaging | [
"Nico Disch",
"David Zimmerer"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1phOqr675L | @inproceedings{
tang2023solving,
title={Solving Noisy Inverse Problems via Posterior Sampling: A Policy Gradient View-Point},
author={Haoyue Tang and Tian Xie and Aosong Feng and Hanyu Wang and Chenyang Zhang and Yang Bai},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=1phOqr675L}
} | Solving image inverse problems (e.g., super-resolution and inpainting) requires generating a high fidelity image that matches the given input (the low-resolution image or the masked image). By using the input image as guidance, we can leverage a pretrained diffusion generation model to solve a wide range of image inversion tasks without task specific model fine-tuning. In this work, we propose diffusion policy gradient (DPG), a tractable computation method to estimate the score function given the guidance image. Our method is robust to both Gaussian and Poisson noise added to the input image, and it improves the image restoration consistency and quality on FFHQ, ImageNet and LSUN datasets on both linear and non-linear image inversion tasks (inpainting, super-resolution, motion deblur, non-linear deblur, etc.). | Solving Noisy Inverse Problems via Posterior Sampling: A Policy Gradient View-Point | [
"Haoyue Tang",
"Tian Xie",
"Aosong Feng",
"Hanyu Wang",
"Chenyang Zhang",
"Yang Bai"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=1J9xoPFaKU | @inproceedings{
kapoor2023neural,
title={Neural oscillators for generalizing parametric {PDE}s},
author={Taniya Kapoor and Abhishek Chandra and Daniel Tartakovsky and Hongrui Wang and Alfredo N{\'u}{\~n}ez and Rolf Dollevoet},
booktitle={The Symbiosis of Deep Learning and Differential Equations III},
year={2023},
url={https://openreview.net/forum?id=1J9xoPFaKU}
} | Parametric partial differential equations (PDEs) are ubiquitous in various scientific and engineering fields, manifesting the behavior of systems under varying parameters. Predicting solutions over a parametric space is desirable but prohibitively costly and challenging. In addition, recent neural PDE solvers are usually limited to interpolation scenarios, where solutions are predicted for inputs within the support of the training set. This work proposes to utilize neural oscillators to extend predictions for parameters beyond the trained regime, effectively extrapolating the parametric space. The proposed methodology is validated on three parametric PDEs: linear advection, viscous burgers, and nonlinear heat. The results underscore the promising potential of neural oscillators in extrapolation scenarios for both linear and nonlinear parametric PDEs. | Neural oscillators for generalizing parametric PDEs | [
"Taniya Kapoor",
"Abhishek Chandra",
"Daniel Tartakovsky",
"Hongrui Wang",
"Alfredo Núñez",
"Rolf Dollevoet"
] | Workshop/DLDE | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=zzJNdbfeeW | @inproceedings{
wang2023removing,
title={Removing Biases from Molecular Representations via Information Maximization},
author={Chenyu Wang and Sharut Gupta and Caroline Uhler and Tommi Jaakkola},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=zzJNdbfeeW}
} | High-throughput drug screening -- using cell imaging or gene expression measurements as readouts of drug effect -- is a critical tool in biotechnology to assess and understand the relationship between the chemical structure and biological activity of a drug. Since large-scale screens have to be divided into multiple experiments, a key difficulty is dealing with batch effects, which can introduce systematic errors and non-biological associations in the data. We propose InfoCORE, an Information maximization approach for COnfounder REmoval, to effectively deal with batch effects and obtain refined molecular representations. InfoCORE establishes a variational lower bound on the conditional mutual information of the latent representations given a batch identifier. It adaptively reweighs samples to equalize their implied batch distribution. Extensive experiments on drug screening data reveal InfoCORE's superior performance in a multitude of tasks including molecular property prediction and molecule-phenotype retrieval. Additionally, we show results for how InfoCORE offers a versatile framework and resolves general distribution shifts and issues of data fairness by minimizing correlation with spurious features or removing sensitive attributes. The code is available at https://github.com/uhlerlab/InfoCORE. | Removing Biases from Molecular Representations via Information Maximization | [
"Chenyu Wang",
"Sharut Gupta",
"Caroline Uhler",
"Tommi Jaakkola"
] | Workshop/AI4D3 | 2312.00718 | [
"https://github.com/uhlerlab/infocore"
] | https://huggingface.co/papers/2312.00718 | 1 | 0 | 0 | 4 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=yWO07xH7PB | @inproceedings{
lin2023molsiam,
title={MolSiam: Simple Siamese Self-supervised Representation Learning for Small Molecules},
author={Joshua Yao-Yu Lin},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=yWO07xH7PB}
} | We investigate a self-supervised learning technique from the Simple Siamese (SimSiam) Representation Learning framework on 2D molecule graphs. SimSiam does not require negative samples during training, making it 1) more computationally efficient and 2) less vulnerable to faulty negatives compared with contrastive learning. Leveraging unlabeled molecular data, we demonstrate that our approach, MolSiam, effectively captures the underlying features of molecules and shows that those with similar properties tend to cluster in UMAP analysis. By fine-tuning pre-trained MolSiam models, we observe performance improvements across four downstream therapeutic property prediction tasks without training with negative pairs. | MolSiam: Simple Siamese Self-supervised Representation Learning for Small Molecules | [
"Joshua Yao-Yu Lin"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ySUsZqIskj | @inproceedings{
wu2023multitaskguided,
title={Multitask-Guided Self-Supervised Tabular Learning for Patient-Specific Survival Prediction},
author={You Wu and Omid Bazgir and Yongju Lee and Tommaso Biancalani and James Lu and Ehsan Hajiramezanali},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=ySUsZqIskj}
} | Survival prediction, central to the analysis of clinical trials, has the potential to be transformed by the availability of RNA-seq data as it reveals the underlying molecular and genetic mechanisms for disease and outcomes. However, the amount of RNA-seq samples available for understudied or rare diseases is often limited. To address this, leveraging data across different cancer types can be a viable solution, necessitating the application of self-supervised learning techniques. Yet, this wealth of data often comes in a tabular format without a known structure, hindering the development of a generally effective augmentation method for survival prediction. While traditional methods have been constrained by a one cancer-one model philosophy or have relied solely on a single modality, our approach, Guided-STab, on the contrary, offers a comprehensive approach through pretraining on all available RNA-seq data from various cancer types while guiding the representation by incorporating sparse clinical features as auxiliary tasks. With a multitask-guided self-supervised representation learning framework, we maximize the potential of vast unlabeled datasets from various cancer types, leading to genomic-driven survival predictions. These auxiliary clinical tasks then guide the learned representations to enhance critical survival factors. Extensive experiments reinforce the promise of our approach, as Guided-STab consistently outperforms established benchmarks on TCGA dataset. | Multitask-Guided Self-Supervised Tabular Learning for Patient-Specific Survival Prediction | [
"You Wu",
"Omid Bazgir",
"Yongju Lee",
"Tommaso Biancalani",
"James Lu",
"Ehsan Hajiramezanali"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=yOmTE8XCyF | @inproceedings{
torrisi2023do,
title={Do chemical language models provide a better compound representation?},
author={Mirko Torrisi and Saeid Asadollahi and Antonio De la Vega de Leon and Kai Wang and Wilbert Copeland},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=yOmTE8XCyF}
} | In recent years, several chemical language models have been developed, inspired by the success of protein language models and advancements in natural language processing. In this study, we explore whether pre-training a chemical language model on billion-scale compound datasets, such as Enamine and ZINC20, can lead to improved compound representation in the drug space. We compare the learned representations of these models with de the facto standard compound representation, and evaluate their potential application in drug discovery and development by benchmarking them on biophysics, physiology, and physical chemistry datasets. Our findings suggest that the conventional masked language modeling approach on these extensive pre-training datasets is insufficient in enhancing compound representations. This highlights the need for additional physicochemical inductive bias in the modeling beyond scaling the dataset size. | Do chemical language models provide a better compound representation? | [
"Mirko Torrisi",
"Saeid Asadollahi",
"Antonio De la Vega de Leon",
"Kai Wang",
"Wilbert Copeland"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=w59xrN8ICQ | @inproceedings{
kong2023generalist,
title={Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning},
author={Xiangzhe Kong and Wenbing Huang and Yang Liu},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=w59xrN8ICQ}
} | Many processes in biology and drug discovery involve various 3D interactions between molecules, such as protein and protein, protein and small molecule, etc. Given that different molecules are usually represented in different granularity, existing methods usually encode each type of molecules independently with different models, leaving it defective to learn the universal underlying interaction physics. In this paper, we first propose to universally represent an arbitrary 3D complex as a geometric graph of sets, shedding light on encoding all types of molecules with one model. We then propose a Generalist Equivariant Transformer (GET) to effectively capture both domain-specific hierarchies and domain-agnostic interaction physics. To be specific, GET consists of a bilevel attention module, a feed-forward module and a layer normalization module, where each module is E(3) equivariant and specialized for handling sets of variable sizes. Notably, in contrast to conventional pooling-based hierarchical models, our GET is able to retain fine-grained information of all levels. Extensive experiments on the interactions between proteins, small molecules and RNA/DNAs verify the effectiveness and generalization capability of our proposed method across different domains. | Generalist Equivariant Transformer Towards 3D Molecular Interaction Learning | [
"Xiangzhe Kong",
"Wenbing Huang",
"Yang Liu"
] | Workshop/AI4D3 | 2306.01474 | [
"https://github.com/thunlp-mt/get"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=tXKSsUeseq | @inproceedings{
kozlova2023inpainting,
title={Inpainting Protein Sequence and Structure with ProtFill},
author={Elizaveta Kozlova and Arthur Valentin and Daniel Nakhaee-Zadeh Gutierrez},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=tXKSsUeseq}
} | Designing new proteins with specific binding capabilities is a challenging task that has the potential to revolutionize many fields, including medicine and material science. Here we introduce ProtFill, a novel method for the simultaneous design of protein structures and sequences. Employing an $SE(3)$ equivariant diffusion graph neural network, our method excels in both sequence prediction and structure recovery compared to SOTA models. We incorporate edge feature updates in GVP-GNN message passing layers to refine our design process. The model's applicability for the interface redesign task is showcased for antibodies as well as other proteins. The code is available at https://github.com/adaptyvbio/ProtFill. | Inpainting Protein Sequence and Structure with ProtFill | [
"Elizaveta Kozlova",
"Arthur Valentin",
"Daniel Nakhaee-Zadeh Gutierrez"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=rNUnPbHilv | @inproceedings{
shanehsazzadeh2023textitin,
title={\${\textbackslash}textit\{In vitro\}\$ validated antibody design against multiple therapeutic antigens using generative inverse folding},
author={Amir Pouya Shanehsazzadeh},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=rNUnPbHilv}
} | Deep learning approaches have demonstrated the ability to design protein sequences given backbone structures. While these approaches have been applied $\textit{in silico}$ to designing antibody complementarity-determining regions (CDRs), they have yet to be validated $\textit{in vitro}$ for designing antibody binders, which is the true measure of success for antibody design. Here we describe $\textit{IgDesign}$, a deep learning method for antibody CDR design, and demonstrate its robustness with successful binder design for 8 therapeutic antigens. The model is tasked with designing heavy chain CDR3 (HCDR3) or all three heavy chain CDRs (HCDR123) using native backbone structures of antibody-antigen complexes, along with the antigen and antibody framework (FWR) sequences as context. For each of the 8 antigens, we design 100 HCDR3s and 100 HCDR123s, scaffold them into the native antibody's variable region, and screen them for binding against the antigen using surface plasmon resonance (SPR). As a baseline, we screen 100 HCDR3s taken from the model's training set and paired with the native HCDR1 and HCDR2. We observe that both HCDR3 design and HCDR123 design outperform this HCDR3-only baseline. IgDesign is the first experimentally validated antibody inverse folding model. It can design antibody binders to multiple therapeutic antigens with high success rates and, in some cases, improved affinities over clinically validated reference antibodies. Antibody inverse folding has applications to both $\textit{de novo}$ antibody design and lead optimization, making IgDesign a valuable tool for accelerating drug development and enabling therapeutic design. | In vitro validated antibody design against multiple therapeutic antigens using generative inverse folding | [
"Amir Pouya Shanehsazzadeh"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=qj1Di608kj | @inproceedings{
thieme2023topopool,
title={TopoPool: An Adaptive Graph Pooling Layer for Extracting Molecular and Protein Substructures},
author={Mattson Thieme and Majdi Hassan and Chetan Rupakheti and Kedar Balaji Thiagarajan and Abhishek Pandey and Han Liu},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=qj1Di608kj}
} | Within molecules and proteins, discrete substructures affect high level properties and behavior in distinct ways. As such, explicitly locating and accounting for these substructures is a central problem when learning molecular or protein representations. Typically represented as graphs, this task falls under the umbrella of graph pooling, or segmentation. Given the highly variable size, number, and topology of these substructures, an ideal pooling algorithm would would adapt on a graph-by-graph basis and use local context to locate optimal pools. However, this poses a challenge where differentiability is concerned, and each of the learnable graph pooling methods proposed to date must make strong a priori assumptions in regards to the number or size of the learned pools. As such, demand remains for a graph pooling algorithm that can maintain differentiability while retaining adaptability in the size and number of learned pools. To meet this demand, we introduce the Topographical Pooling Layer (TopoPool): a differentiable, hierarchical graph pooling layer that learns an arbitrary number of varying sized pools without making any a priori assumptions about their number or size. Additionally, it naturally uncovers only connected substructures, increasing the interpretability of the learned pools and obviating the need for exogenous regularizers to enforce connectedness. We evaluate TopoPool on diverse molecular and protein property prediction tasks, where we achieve competitive performance against existing methods. Taken together, TopoPool represents a novel addition to the graph pooling toolbox, and is particularly relevant to areas like drug design where locating and optimizing discrete, connected molecular substructures is of central importance. | TopoPool: An Adaptive Graph Pooling Layer for Extracting Molecular and Protein Substructures | [
"Mattson Thieme",
"Majdi Hassan",
"Chetan Rupakheti",
"Kedar Balaji Thiagarajan",
"Abhishek Pandey",
"Han Liu"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=olMBz7gxls | @inproceedings{
tarasov2023offline,
title={Offline {RL} for generative design of protein binders},
author={Denis Tarasov and Ulrich Armel Mbou Sob and Miguel Arbes{\'u} and Nima H. Siboni and Sebastien Boyer and Andries Petrus Smit and Oliver Bent and Arnu Pretorius and Marcin J. Skwark},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=olMBz7gxls}
} | Offline Reinforcement Learning (RL) offers a compelling avenue for solving RL problems without the need for interactions with an environment, which may be expensive or unsafe. While online RL methods have found success in various domains, such as de novo Structure-Based Drug Discovery (SBDD), they struggle when it comes to optimizing essential properties derived from protein-ligand docking. The high computational cost associated with the docking process makes it impractical for online RL, which typically requires hundreds of thousands of interactions during learning. In this study, we propose the application of offline RL to address the bottleneck posed by the docking process, leveraging RL's capability to optimize non-differentiable properties. Our preliminary investigation focuses on using offline RL to conditionally generate drugs with improved docking and chemical properties. | Offline RL for generative design of protein binders | [
"Denis Tarasov",
"Ulrich Armel Mbou Sob",
"Miguel Arbesú",
"Nima H. Siboni",
"Sebastien Boyer",
"Andries Petrus Smit",
"Oliver Bent",
"Arnu Pretorius",
"Marcin J. Skwark"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=oRCo0RKAMA | @inproceedings{
park2023online,
title={Online Learning of Optimal Prescriptions under Bandit Feedback with Unknown Contexts},
author={Hongju Park and Mohamad Kazem Shirani Faradonbeh},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=oRCo0RKAMA}
} | Contextual bandits constitute a classical framework for decision-making under uncertainty. In this setting, the goal is to learn prescriptions of highest reward subject to the contextual information, while the unknown reward parameters of each prescription need to be learned by experimenting it. Accordingly, a fundamental problem is that of balancing exploration (i.e., prescribing different options to learn the parameters), versus exploitation (i.e., sticking with the best option to gain reward). To study this problem, the existing literature mostly considers perfectly observed contexts. However, the setting of partially observed contexts remains unexplored to date, despite being theoretically more general and practically more versatile. We study bandit policies for learning to select optimal prescriptions based on observations, which are noisy linear functions of the unobserved context vectors. Our theoretical analysis shows that the Thompson sampling policy successfully balances exploration and exploitation. Specifically, we establish (i) regret bounds that grow poly-logarithmically with time, (ii) square-root consistency of parameter estimation, and (iii) scaling with other quantities including dimensions and number of options. Extensive numerical experiments with both real and synthetic data are presented as well, corroborating the efficacy of Thompson sampling. To establish the results, we utilize concentration inequalities for dependent data and also develop novel probabilistic bounds for time-varying suboptimality gaps, among others. These techniques pave the road towards studying similar problems. | Online Learning of Optimal Prescriptions under Bandit Feedback with Unknown Contexts | [
"Hongju Park",
"Mohamad Kazem Shirani Faradonbeh"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=nYTeNDMz0E | @inproceedings{
xu2023hit,
title={Hit Expansion Driven By Machine Learning},
author={Jin Xu and Steven Kearnes and JW Feng},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=nYTeNDMz0E}
} | Recent work \cite{McCloskey2020-es} utilized experimental data from DNA-encoded library (DEL) selections to train graph convolutional neural networks (GCNNs) \cite{Kearnes2016-sk} for identifying hit compounds for protein targets and their prospective test results demonstrated excellent hit rates for three diverse proteins. Building on this work, we propose two novel approaches to leverage DEL GCNN model predictions and embeddings to automate hit expansion, a critical step in real-world drug discovery that guides the optimization of initial hit compounds toward clinical candidates. We prospectively tested the proposed approaches on a protein target (sEH) and our methods identified more small molecules with higher potency compared to traditional molecular fingerprint similarity searches. Specifically, we discovered $34$ molecules with higher potency than a sEH clinical trial candidate using our approaches. All sEH assay results are publicly available at \url{https://www.tdcommons.org/dpubs_series/6300/ }. Furthermore, applying the automated hit expansion approach to WDR91, a novel protein target that has no known binders, led to the discovery of two first-in-class covalent binders that were experimentally confirmed by co-crystal structures. | Hit Expansion Driven By Machine Learning | [
"Jin Xu",
"Steven Kearnes",
"JW Feng"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=k2PA7CUUJH | @inproceedings{
didi2023a,
title={A framework for conditional diffusion modelling with applications in motif scaffolding for protein design},
author={Kieran Didi and Francisco Vargas and Simon Mathis and Vincent Dutordoir and Emile Mathieu and Urszula Julia Komorowska and Pietro Lio},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=k2PA7CUUJH}
} | Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision. Generative modelling paradigms based on denoising diffusion processes emerged as a leading candidate to address this motif scaffolding problem and have shown early experimental success in some cases.
In the diffusion paradigm, motif scaffolding is treated as a conditional generation task, and several conditional generation protocols were proposed or imported from the Computer Vision literature.
However, most of these protocols are motivated heuristically, e.g. via analogies to Langevin dynamics, and lack a unifying framework, obscuring connections between the different approaches.
In this work, we unify conditional training and conditional sampling procedures under one common framework based on the mathematically well-understood Doob's h-transform. This new perspective allows us to draw connections between existing methods and propose a new conditional training protocol. We illustrate the effectiveness of this new protocol in both, image outpainting and motif scaffolding and find that it outperforms standard methods. | A framework for conditional diffusion modelling with applications in motif scaffolding for protein design | [
"Kieran Didi",
"Francisco Vargas",
"Simon Mathis",
"Vincent Dutordoir",
"Emile Mathieu",
"Urszula Julia Komorowska",
"Pietro Lio"
] | Workshop/AI4D3 | 2312.09236 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=j79SDXZQiI | @inproceedings{
ajileye2023automating,
title={Automating reward function configuration for drug design},
author={Temitope Ajileye and Paul Gainer and Marius Urbonas and Douglas Eduardo Valente Pires},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=j79SDXZQiI}
} | Designing reward functions that can guide generative molecular design (GMD) algorithms to desirable areas of chemical space is of critical importance in AI-driven drug discovery. Traditionally, this has been a manual and error-prone task; the selection of appropriate computational methods to approximate biological assays is challenging and the normalisation and aggregation of computed values into a single score even more so, leading to potential reliance on trial-and-error approaches. We propose a novel approach for automated reward configuration that relies solely on experimental data, mitigating the challenges of manual reward adjustment on drug discovery projects. Our method achieves this by constructing a ranking over experimental data based on Pareto dominance over the multi-objective space, then training a neural network to approximate the reward function such that rankings determined by the predicted reward correlate with those determined by the Pareto dominance relation. We validate our method using two case studies. In the first study we simulate Design-Make-Test-Analyse (DMTA) cycles by alternating reward function updates and generative runs guided by that function. We show that the learned function adapts over time to yield compounds that score highly with respect to evaluation functions taken from the literature. In the second study we apply our algorithm to historical data from four real drug discovery projects. We show that our algorithm yields reward functions that outperform the predictive accuracy of human-defined functions, achieving an improvement of up to $0.4$ in Spearman's correlation against a ground truth evaluation function that encodes the target drug profile for that project. Our method provides an efficient data-driven way to configure reward functions for GMD, and serves as a strong baseline for future research into transformative approaches for the automation of drug discovery. | Automating reward function configuration for drug design | [
"Temitope Ajileye",
"Paul Gainer",
"Marius Urbonas",
"Douglas Eduardo Valente Pires"
] | Workshop/AI4D3 | 2312.09865 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=fLPHUS8j1T | @inproceedings{
lau2023dgfn,
title={{DGFN}: Double Generative Flow Networks},
author={Elaine Lau and Nikhil Murali Vemgal and Doina Precup and Emmanuel Bengio},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=fLPHUS8j1T}
} | Deep learning is emerging as an effective tool in drug discovery, with potential applications in both predictive and generative models. Generative Flow Networks (GFlowNets/GFNs) are a recently introduced method recognized for the ability to generate diverse candidates, in particular in small molecule generation tasks. In this work, we introduce double GFlowNets (DGFNs). Drawing inspiration from reinforcement learning and Double Deep Q-Learning, we introduce a target network used to sample trajectories, while updating the main network with these sampled trajectories. Empirical results confirm that DGFNs effectively enhance exploration in sparse reward domains and high-dimensional state spaces, both challenging aspects of de-novo design in drug discovery. | DGFN: Double Generative Flow Networks | [
"Elaine Lau",
"Nikhil Murali Vemgal",
"Doina Precup",
"Emmanuel Bengio"
] | Workshop/AI4D3 | 2310.19685 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=cbCimtxKy0 | @inproceedings{
shen2023tacogfn,
title={Taco{GFN}: Target Conditioned {GF}lowNet for Drug Design},
author={Tony Shen and Mohit Pandey and Jason Smith and Artem Cherkasov and Martin Ester},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=cbCimtxKy0}
} | We seek to automate the generation of drug-like compounds conditioned to specific protein pocket targets. Most current methods approximate the protein-molecule distribution of a finite dataset and, therefore struggle to generate molecules with significant binding improvement over the training dataset. We instead frame the pocket-conditioned molecular generation task as an RL problem and develop TacoGFN, a target conditional Generative Flow Networks model. Our method is explicitly encouraged to generate molecules with desired properties as opposed to fitting on a pre-existing data distribution. To this end, we develop transformer-based docking score prediction to speed up docking score computation and propose TacoGFN to explore molecule space efficiently. Furthermore, we incorporate several rounds of active learning where generated samples are queried using a docking oracle to improve the docking score prediction. This approach allows us to accurately explore as much of the molecule landscape as we can afford computationally. Empirically, molecules generated using TacoGFN and its variants significantly outperform all baseline methods across every property (Docking score, QED, SA, Lipinski), while being orders of magnitude faster. | TacoGFN: Target Conditioned GFlowNet for Drug Design | [
"Tony Shen",
"Mohit Pandey",
"Jason Smith",
"Artem Cherkasov",
"Martin Ester"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ZhBwSxbJbu | @inproceedings{
cede{\~n}o2023towards,
title={Towards a more inductive world for drug repurposing approaches},
author={Jesus de la Fuente Cede{\~n}o and Guillermo Serrano and Ux{\'\i}a Veleiro and Mikel Casals and Laura Vera and Marija Pizurica and Antonio Pineda-Lucena and Idoia Ochoa and Silve Vicent and Olivier Gevaert and Mikel Hernaez},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=ZhBwSxbJbu}
} | Drug-target interaction (DTI) prediction is a challenging, albeit essential task in drug repurposing. Learning on graph models have drawn special attention as they can significantly reduce drug repurposing costs and time commitment. However, many current approaches require high-demanding additional information besides DTIs that complicates their evaluation process and usability. Additionally, structural differences in the learning architecture of current models hinder their fair benchmarking. In this work, we first perform an in-depth evaluation of current DTI datasets and prediction models through a robust benchmarking process, and show that DTI prediction methods based on transductive models lack generalization and lead to inflated performance when evaluated as previously done in the literature, hence not being suited for drug repurposing approaches. We then propose a novel biologically-driven strategy for negative edge subsampling and show through in vitro validation that newly discovered interactions are indeed true. We envision this work as the underpinning for future fair benchmarking and robust model design. All generated resources and tools are publicly available as a python package. | Towards a more inductive world for drug repurposing approaches | [
"Jesus de la Fuente Cedeño",
"Guillermo Serrano",
"Uxía Veleiro",
"Mikel Casals",
"Laura Vera",
"Marija Pizurica",
"Antonio Pineda-Lucena",
"Idoia Ochoa",
"Silve Vicent",
"Olivier Gevaert",
"Mikel Hernaez"
] | Workshop/AI4D3 | 2311.12670 | [
"https://github.com/ubioinformat/graphemb"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=ZT0V2VVDJc | @inproceedings{
wang2023sampleefficient,
title={Sample-efficient Antibody Design through Protein Language Model for Risk-aware Batch Bayesian Optimization},
author={Yanzheng Wang and Tianyu Shi},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=ZT0V2VVDJc}
} | Antibody design is a time-consuming and expensive process that often requires1
extensive experimentation to identify the best candidates. To address this challenge,2
we propose an efficient and risk-aware antibody design framework that leverages3
protein language models (PLMs) and batch Bayesian optimization (BO). Our4
framework utilizes the generative power of protein language models to predict5
candidate sequences with higher naturalness and a Bayesian optimization algorithm6
to iteratively explore the sequence space and identify the most promising candidates.7
To further improve the efficiency of the search process, we introduce a risk-aware8
approach that balances exploration and exploitation by incorporating uncertainty9
estimates into the acquisition function of the Bayesian optimization algorithm.10
We demonstrate the effectiveness of our approach through experiments on several11
benchmark datasets, showing that our framework outperforms state-of-the-art12
methods in terms of both efficiency and quality of the designed sequences. Our13
framework has the potential to accelerate the discovery of new antibodies and14
reduce the cost and time required for antib | Sample-efficient Antibody Design through Protein Language Model for Risk-aware Batch Bayesian Optimization | [
"Yanzheng Wang",
"Tianyu Shi"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=YXchfo0LlX | @inproceedings{
dreyer2023de,
title={De novo design of antibody heavy chains with {SE}(3) diffusion},
author={Frederic A Dreyer and Daniel Cutting and David Errington and Constantin Schneider and Charlotte Deane},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=YXchfo0LlX}
} | We introduce VH-Diff, an antibody heavy chain variable domain diffusion model.
This model is based on FrameDiff, a general protein backbone diffusion framework, which was fine-tuned on antibody structures.
The backbone dihedral angles of sampled structures show good agreement with a reference antibody distribution.
We use an antibody-specific inverse folding model to recover sequences corresponding to the predicted structures, and study their validity with an antibody numbering tool.
Assessing the designability and novelty of the structures generated with our heavy chain model we find that VH-Diff produces highly designable structures that can contain novel binding regions.
Finally, we compare our model with a state-of-the-art sequence-based generative model and show more consistent preservation of the conserved framework region with our structure-based method. | De novo design of antibody heavy chains with SE(3) diffusion | [
"Frederic A Dreyer",
"Daniel Cutting",
"David Errington",
"Constantin Schneider",
"Charlotte Deane"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=YI85Jrvpj0 | @inproceedings{
adebayo2023identifying,
title={Identifying regularization schemes that make feature attributions faithful},
author={Julius Adebayo and Samuel Don Stanton and Simon Kelow and Michael Maser and Richard Bonneau and Vladimir Gligorijevic and Kyunghyun Cho and Stephen Ra and Nathan C. Frey},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=YI85Jrvpj0}
} | Feature attribution methods assign a score to each input dimension as a measure of the relevance of that dimension to a model's output. Despite wide use, the feature importance rankings induced by gradient-based feature attributions are unfaithful, that is, they do not correlate with the input-perturbation sensitivity of the model---unless the model is trained to be adversarially robust. Here we demonstrate that these concerns translate to models trained for protein function prediction tasks. Despite making a model's gradient-based attributions faithful to the model, adversarial training has low real-data performance. We find that independent Gaussian noise corruption is an effective alternative, to adversarial training, that confers faithfulness onto a model's gradient-based attributions without performance degradation. On the other hand, we observe no meaningful faithfulness benefits from regularization schemes like dropout and weight decay. We translate these insights to a real-world protein function prediction task, where the gradient-based feature attributions of noise-regularized models, correctly indicate low sensitivity to irrelevant gap tokens in a protein's sequence alignment. | Identifying regularization schemes that make feature attributions faithful | [
"Julius Adebayo",
"Samuel Don Stanton",
"Simon Kelow",
"Michael Maser",
"Richard Bonneau",
"Vladimir Gligorijevic",
"Kyunghyun Cho",
"Stephen Ra",
"Nathan C. Frey"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=TdV9PaTvCS | @inproceedings{
calvi2023leap,
title={Leap: molecular synthesisability scoring with intermediates},
author={Antonia Calvi and Th{\'e}ophile Gaudin and Dominik Miketa and Dominique Sydow and Liam Wilbraham},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=TdV9PaTvCS}
} | Assessing whether a molecule can be synthesised is a primary task in drug discovery. It enables computational chemists to filter for viable compounds
or bias molecular generative models. The notion of synthesisability is dynamic as it evolves depending on the availability of key compounds. A common approach in drug discovery involves exploring the chemical space surrounding synthetically-accessible
intermediates. This strategy improves the synthesisability of the derived molecules due to the availability of key intermediates.
Existing synthesisability scoring methods such as SAScore, SCScore and RAScore, cannot condition on intermediates dynamically.
Our approach, Leap, is a GPT-2 model trained on the depth, or longest linear path, of predicted synthesis routes that allows information on the availability of key intermediates to be included at inference time.
We show that Leap surpasses all other scoring methods by at least 5% on AUC score when identifying synthesisable molecules, and can successfully adapt predicted scores when presented with a relevant intermediate compound. | Leap: molecular synthesisability scoring with intermediates | [
"Antonia Calvi",
"Théophile Gaudin",
"Dominik Miketa",
"Dominique Sydow",
"Liam Wilbraham"
] | Workshop/AI4D3 | 2403.13005 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=TBk3igaqL1 | @inproceedings{
seo2023pharmaconet,
title={PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling},
author={Seonghwan Seo and Woo Youn Kim},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=TBk3igaqL1}
} | As the size of accessible compound libraries expands to over 10 billion, the need for more efficient structure-based virtual screening methods is emerging. Different pre-screening methods have been developed for rapid screening, but there is still a lack of structure-based methods applicable to various proteins that perform protein-ligand binding conformation prediction and scoring in an extremely short time. Here, we describe for the first time a deep-learning framework for structure-based pharmacophore modeling to address this challenge. We frame pharmacophore modeling as an instance segmentation problem to determine each protein hotspot and the location of corresponding pharmacophores, and protein-ligand binding pose prediction as a graph-matching problem. PharmacoNet is significantly faster than state-of-the-art structure-based approaches, yet reasonably accurate with a simple scoring function. Furthermore, we show the promising result that PharmacoNet effectively retains hit candidates even under the high pre-screening filtration rates. Overall, our study uncovers the hitherto untapped potential of a pharmacophore modeling approach in deep learning-based drug discovery. | PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling | [
"Seonghwan Seo",
"Woo Youn Kim"
] | Workshop/AI4D3 | 2310.00681 | [
"https://github.com/seonghwanseo/molvoxel"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=S3jU99U2k5 | @inproceedings{
jin2023modelfree,
title={Model-free selective inference and its applications to drug discovery},
author={Ying Jin and Emmanuel Candes},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=S3jU99U2k5}
} | Decision making or scientific discovery pipelines such as drug discovery often involve multiple stages: before any resource-intensive step, there is often an initial screening that uses predictions from a machine learning model to shortlist a few candidates from a large pool. We study screening procedures that aim to select candidates whose unobserved outcomes exceed user-specified values. We develop a method that wraps around any prediction model to produce a subset of candidates while controlling the proportion of falsely selected units. Building upon the conformal inference framework, our method first constructs p-values that quantify the statistical evidence for large outcomes; it then determines the shortlist by comparing the p-values to a threshold introduced in the multiple testing literature. In many cases, the procedure selects candidates whose predictions are above a data-dependent threshold. Our theoretical guarantee holds under mild exchangeability conditions on the samples, generalizing existing results on multiple conformal p-values. We demonstrate the empirical performance of our method via applications to drug discovery datasets. | Model-free selective inference and its applications to drug discovery | [
"Ying Jin",
"Emmanuel Candes"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Pw9ecLvwFZ | @inproceedings{
jing2023alphafold,
title={AlphaFold Meets Flow Matching for Generating Protein Ensembles},
author={Bowen Jing and Bonnie Berger and Tommi Jaakkola},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=Pw9ecLvwFZ}
} | Recent breakthroughs in protein structure prediction have pointed to structural ensembles as the next frontier in the computational understanding of protein structure. At the same time, iterative refinement techniques such as diffusion have driven significant advancements in generative modeling. We explore the synergy of these developments by combining AlphaFold and ESMFold with flow matching, a powerful modern generative modeling framework, in order to sample the conformational landscape of proteins. When trained on the PDB and evaluated on proteins with multiple recent structures, our method produces ensembles with similar precision and greater diversity compared to MSA subsampling. When further fine-tuned on coarse-grained molecular dynamics trajectories, our model generalizes to unseen proteins and accurately predicts conformational flexbility, captures the joint distribution of atomic positions, and models higher-order physiochemical properties such as intermittent contacts and solvent exposure. These results open exciting avenues in the computational prediction of conformational flexibility. | AlphaFold Meets Flow Matching for Generating Protein Ensembles | [
"Bowen Jing",
"Bonnie Berger",
"Tommi Jaakkola"
] | Workshop/AI4D3 | 2402.04845 | [
"https://github.com/bjing2016/alphaflow"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=PsVZK0hvWk | @inproceedings{
igashov2023retrobridge,
title={RetroBridge: Modeling Retrosynthesis with Markov Bridges},
author={Ilia Igashov and Arne Schneuing and Marwin Segler and Michael M. Bronstein and Bruno Correia},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=PsVZK0hvWk}
} | Retrosynthesis planning is a fundamental challenge in chemistry which aims at designing multi-step reaction pathways from commercially available starting materials to a target molecule. Each step in multi-step retrosynthesis planning requires accurate prediction of possible precursor molecules given the target molecule and confidence estimates to guide heuristic search algorithms. We model single-step retrosynthesis as a distribution learning problem in a discrete state space. First, we introduce the Markov Bridge Model, a generative framework aimed to approximate the dependency between two intractable discrete distributions accessible via a finite sample of coupled data points. Our framework is based on the concept of a Markov bridge, a Markov process pinned at its endpoints. Unlike diffusion-based methods, our Markov Bridge Model does not need a tractable noise distribution as a sampling proxy and directly operates on the input product molecules as samples from the intractable prior distribution. We then address the retrosynthesis planning problem with our novel framework and introduce RetroBridge, a template-free retrosynthesis modeling approach that achieves state-of-the-art results on standard evaluation benchmarks. | RetroBridge: Modeling Retrosynthesis with Markov Bridges | [
"Ilia Igashov",
"Arne Schneuing",
"Marwin Segler",
"Michael M. Bronstein",
"Bruno Correia"
] | Workshop/AI4D3 | 2308.16212 | [
"https://github.com/igashov/retrobridge"
] | https://huggingface.co/papers/2308.16212 | 0 | 0 | 0 | 5 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=OrET0hHwLy | @inproceedings{
lee2023abdeepga,
title={Ab-Deep{GA}: A generative modeling framework leveraging deep learning for antibody affinity tuning},
author={BoRam Lee and Yara Seif and Kevin Teng and Xiao Xiao and Isha Verma and Ming-Tang Chen and Alan C Cheng},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=OrET0hHwLy}
} | Antibodies and their derived biologics are a major class of novel human therapeutics, with over 70 FDA approvals in the past decade. $In$ $vitro$ display technologies are commonly used to select specific antibodies with high affinity and specificity to a target antigen, but these experiments are resource intensive and can explore only a limited antibody sequence space. Here, we present Ab-DeepGA, a method that combines experimental advances with a deep learning interpretability approach to efficiently search sequence space for sequences with desired affinity to a target antigen. Starting from a combined phage-yeast display experiment against a target antigen, we sorted and sequenced antigen-specific, llama-derived heavy-chain only antibodies ($V_{HH}$) with a wide range of binding affinities. This data was used to train a deep convolutional neural network to predict $V_{HH}$ binding strength from sequence. To generate $de$ $novo$ sequences at a desired binding strength, model interpretation was applied to the trained models, and SHAPley interpretation was used to guide genetic algorithm exploration of sequence space. We show our approach leads to improved recovery of sequences in a held-out test set compared to genetic algorithms. Ab-DeepGA is a novel generative modeling approach that combines advances in experimental display with an interpretable deep learning algorithm that efficiently explores antibody sequence space to identify high affinity binders to a target antigen. | Ab-DeepGA: A generative modeling framework leveraging deep learning for antibody affinity tuning | [
"BoRam Lee",
"Yara Seif",
"Kevin Teng",
"Xiao Xiao",
"Isha Verma",
"Ming-Tang Chen",
"Alan C Cheng"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=OBSVDRXcXN | @inproceedings{
nguyen2023expt,
title={Ex{PT}: Synthetic Pretraining for Few-Shot Experimental Design},
author={Tung Nguyen and Sudhanshu Agrawal and Aditya Grover},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=OBSVDRXcXN}
} | Experimental design is a fundamental problem in many science and engineering fields. In this problem, sample efficiency is crucial due to the time, money, and safety costs of real-world design evaluations. Existing approaches either rely on active data collection or access to large, labeled datasets of past experiments, making them impractical in many real-world scenarios. In this work, we address the more challenging yet realistic setting of few-shot experimental design, where only a few labeled data points of input designs and their corresponding values are available. We approach this problem as a conditional generation task, where a model conditions on a few labeled examples and the desired output to generate an optimal input design. To this end, we introduce Experiment Pretrained Transformers (ExPT), a foundation model for few-shot experimental design that employs a novel combination of synthetic pretraining with in-context learning. In ExPT, we only assume knowledge of a finite collection of unlabelled data points from the input domain and pretrain a transformer neural network to optimize diverse synthetic functions defined over this domain. Unsupervised pretraining allows ExPT to adapt to any design task at test time in an in-context fashion by conditioning on a few labeled data points from the target task and generating the candidate optima. We evaluate ExPT on few-shot experimental design in challenging domains and demonstrate its superior generality and performance compared to existing methods. The source code is available at https://github.com/tung-nd/ExPT.git. | ExPT: Synthetic Pretraining for Few-Shot Experimental Design | [
"Tung Nguyen",
"Sudhanshu Agrawal",
"Aditya Grover"
] | Workshop/AI4D3 | 2310.19961 | [
"https://github.com/tung-nd/expt"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=O9bzFjQBjg | @inproceedings{
majha2023on,
title={On Modelability and Generalizability: Are Machine Learning Models for Drug Synergy Exploiting Artefacts and Biases in Available Data?},
author={Arushi GK Majha and Ian Stott and Andreas Bender},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=O9bzFjQBjg}
} | Synergy models are useful tools for exploring drug combinatorial search space and identifying promising sub-spaces for in vitro/vivo experiments. Here, we report that distributional biases in the training-validation-test sets used for predictive modeling of drug synergy can explain much of the variability observed in model performances (up to $0.22$ $\Delta$AUPRC). We built 145 classification models spanning 4,577 unique drugs and 75,276 pair-wise drug combinations extracted from DrugComb, and examined spurious correlations in both the input feature and output label spaces. We posit that some synergy datasets are easier to model than others due to factors such as synergy spread, class separation, chemical structural diversity, physicochemical diversity, combinatorial tests per drug, and combinatorial label entropy. We simulate distribution shifts for these dataset attributes and report that the drug-wise homogeneity of combinatorial labels most influences modelability ($0.16\pm0.06$ $\Delta$AUPRC). Our findings imply that seemingly high-performing drug synergy models may not generalize well to broader medicinal space. We caution that the synergy modeling community's efforts may be better expended in examining data-specific artefacts and biases rigorously prior to model building. | On Modelability and Generalizability: Are Machine Learning Models for Drug Synergy Exploiting Artefacts and Biases in Available Data? | [
"Arushi GK Majha",
"Ian Stott",
"Andreas Bender"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=NYbnVDuKuC | @inproceedings{
crivelli-decker2023machine,
title={Machine Learning Guided {AQFEP}: A Fast \& Efficient Absolute Free Energy Perturbation Solution for Virtual Screening},
author={Jordan Crivelli-Decker and Zane Beckwith and Gary Tom and Ly Le and Sheenam Khuttan and Romelia Salomon-Ferrer and Jackson Beall and Andrea Bortolato},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=NYbnVDuKuC}
} | Structure-based methods in drug discovery have become an integral part of the modern drug discovery process. The power of virtual screening lies in its ability to rapidly and cost-effectively explore enormous chemical spaces to select promising ligands for further experimental investigation. Relative Free Energy Perturbation (RFEP) and similar methods are the gold standard for binding affinity prediction in drug discovery hit-to-lead and lead optimization phases, but have high computational cost and the requirement of a structural analog with a known activity. Without a reference molecule requirement, Absolute FEP (AFEP) has, in theory, better accuracy for hit ID, but in practice, the slow throughput is not compatible with VS, where fast docking and unreliable scoring functions are still the standard. Here, we present an integrated workflow to virtually screen large and diverse chemical libraries efficiently, combining active learning with a physics-based scoring function based on a fast absolute free energy perturbation method. We validated the performance of the approach in the ranking of structurally related ligands, virtual screening hit rate enrichment, and active learning chemical space exploration; disclosing the largest reported collection of free energy simulations to date. | Machine Learning Guided AQFEP: A Fast Efficient Absolute Free Energy Perturbation Solution for Virtual Screening | [
"Jordan Crivelli-Decker",
"Zane Beckwith",
"Gary Tom",
"Ly Le",
"Sheenam Khuttan",
"Romelia Salomon-Ferrer",
"Jackson Beall",
"Andrea Bortolato"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=NDBx1lo3jQ | @inproceedings{
kim2023dataefficient,
title={Data-Efficient Molecular Generation with Hierarchical Textual Inversion},
author={Seojin Kim and Jaehyun Nam and Sihyun Yu and Younghoon Shin and Jinwoo Shin},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=NDBx1lo3jQ}
} | Developing an effective molecular generation framework even with a limited number of molecules is often important for its practical deployment, e.g., drug discovery, since acquiring task-related molecular data requires expensive and time-consuming experimental costs. To tackle this issue, we introduce Hierarchical textual Inversion for Molecular Generation (HI-Mol), a novel data-efficient molecular generation method. HI-Mol is inspired by a recent textual inversion technique in the visual domain that achieves data-efficient generation via simple optimization of a new single text token of a pre-trained text-to-image generative model. However, we find that its naive adoption fails for molecules due to their complicated and structured nature. Hence, we propose a hierarchical textual inversion scheme based on introducing low-level tokens that are selected differently per molecule in addition to the original single text token in textual inversion to learn the common concept among molecules. We then generate molecules using a pre-trained text-to-molecule model by interpolating the low-level tokens. Extensive experiments demonstrate the superiority of HI-Mol with notable data-efficiency. For instance, on QM9, HI-Mol outperforms the prior state-of-the-art method with 50$\times$ less training data. We also show the efficacy of HI-Mol in various applications, including molecular optimization and low-shot molecular property prediction. | Data-Efficient Molecular Generation with Hierarchical Textual Inversion | [
"Seojin Kim",
"Jaehyun Nam",
"Sihyun Yu",
"Younghoon Shin",
"Jinwoo Shin"
] | Workshop/AI4D3 | 2405.02845 | [
"https://github.com/seojin-kim/hi-mol"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=N6uLT2MVV1 | @inproceedings{
dubourg-felonneau2023pinui,
title={Pi{NUI}: A Dataset of Protein-Protein Interactions for Machine Learning},
author={Geoffroy Dubourg-Felonneau and Eyal Akiva and Daniel Wesego and Ranjani Varadan},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=N6uLT2MVV1}
} | We introduce a new novel dataset named PiNUI: Protein Interactions with Nearly Uniform Imbalance. PiNUI is a dataset of Protein-Protein Interactions (PPI) specifically designed for Machine Learning (ML) applications that offer a higher degree of representativeness of real-world PPI tasks compared to existing ML-ready PPI datasets. We achieve such by increasing the data size and quality, and minimizing the sampling bias of negative interactions. We demonstrate that models trained on PiNUI almost always outperform those trained on conventional PPI datasets when evaluated on various general PPI tasks using external test sets. | PiNUI: A Dataset of Protein-Protein Interactions for Machine Learning | [
"Geoffroy Dubourg-Felonneau",
"Eyal\tAkiva",
"Daniel Wesego",
"Ranjani Varadan"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=MASqXhMTy7 | @inproceedings{
zhang2023protein,
title={Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence},
author={Shuo Zhang and Lei Xie},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=MASqXhMTy7}
} | Prediction of ligand binding sites of proteins is a fundamental and important task for understanding the function of proteins and screening potential drugs. Most existing methods require experimentally determined protein holo-structures as input. However, such structures can be unavailable on novel or less-studied proteins. To tackle this limitation, we propose LaMPSite, which only takes protein sequences and ligand molecular graphs as input for ligand binding site predictions. The protein sequences are used to retrieve residue-level embeddings and contact maps from the pre-trained ESM-2 protein language model. The ligand molecular graphs are fed into a graph neural network to compute atom-level embeddings. Then we compute and update the protein-ligand interaction embedding based on the protein residue-level embeddings and ligand atom-level embeddings, and the geometric constraints in the inferred protein contact map and ligand distance map. A final pooling on protein-ligand interaction embedding would indicate which residues belong to the binding sites. Without any 3D coordinate information of proteins, our proposed model achieves competitive performance compared to baseline methods that require 3D protein structures when predicting binding sites. Given that less than 50% of proteins have reliable structure information in the current stage, LaMPSite will provide new opportunities for drug discovery. | Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence | [
"Shuo Zhang",
"Lei Xie"
] | Workshop/AI4D3 | 2312.03016 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=M9WREnrXDQ | @inproceedings{
akbarian2023all,
title={All You Need is {LOVE}: Large Optimized Vector Embeddings Network for Drug Repurposing},
author={Sina Akbarian and Sepehr Asgarian and Jouhyun Jeon},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=M9WREnrXDQ}
} | Traditional drug development is a resource-intensive and time-consuming process with a high rate of failure. To expedite this process, researchers have turned to computational approaches to construct comprehensive graphs of drug-disease associations and explore drug repurposing, finding novel therapeutic applications for existing medications. In parallel, the rapid advancement of the machine-learning field, coupled with the evolution of Natural Language Processing, shows capabilities for reasoning and extracting relationships across various domains. In this paper, we introduce LOVENet (Large Optimized Vector Embeddings Network), a new framework maximizing the synergistic effects of knowledge graphs and large language models (LLMs) to discover novel therapeutic uses for pre-existing drugs. Specifically, our approach fuses information from pairs of embedding from Llama2 and heterogeneous knowledge graphs to derive complex relations of drugs and diseases. To empirically validate our methodology, we conducted benchmarking experiments against state-of-the-art algorithms, utilizing three distinct datasets. Our results demonstrate that LOVENet consistently outperforms all other baselines. The code for this project is available at https://github.com/KlickInc/brave-foundry-drug-repurposing. | All You Need is LOVE: Large Optimized Vector Embeddings Network for Drug Repurposing | [
"Sina Akbarian",
"Sepehr Asgarian",
"Jouhyun Jeon"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=LdRmpu41Pt | @inproceedings{
harris2023posecheck,
title={PoseCheck: Generative Models for 3D Structure-based Drug Design Produce Unrealistic Poses},
author={Charles Harris and Kieran Didi and Arian Jamasb and Chaitanya Joshi and Simon Mathis and Pietro Lio and Tom Blundell},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=LdRmpu41Pt}
} | Deep generative models for structure-based drug design (SBDD), where molecule generation is conditioned on a 3D protein pocket, have received considerable interest in recent years. These methods offer the promise of higher-quality molecule generation by explicitly modelling the 3D interaction between a potential drug and a protein receptor. However, previous work has primarily focused on the quality of the generated molecules themselves, with limited evaluation of the 3D \emph{poses} that these methods produce, with most work simply discarding the generated pose and only reporting a ``corrected” pose after redocking with traditional methods. Little is known about whether generated molecules satisfy known physical constraints for binding and the extent to which redocking alters the generated interactions. We introduce \posecheck{}, an extensive analysis of multiple state-of-the-art methods and find that generated molecules have significantly more physical violations and fewer key interactions compared to baselines, calling into question the implicit assumption that providing rich 3D structure information improves molecule complementarity. We make recommendations for future research tackling identified failure modes and hope our benchmark will serve as a springboard for future SBDD generative modelling work to have a real-world impact. Our evaluation suite is easy to use in future 3D SBDD work and is available at \href{https://anonymous.4open.science/r/posecheck-358E/README.md}{\texttt{https://anonymous.4open.science/r/posecheck-358E}}. | PoseCheck: Generative Models for 3D Structure-based Drug Design Produce Unrealistic Poses | [
"Charles Harris",
"Kieran Didi",
"Arian Jamasb",
"Chaitanya Joshi",
"Simon Mathis",
"Pietro Lio",
"Tom Blundell"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KWWbS5yVtl | @inproceedings{
delong2023neurosymbolic,
title={Neurosymbolic {AI} Reveals Biases and Limitations in {ML}-Driven Drug Discovery},
author={Lauren DeLong and Yojana Gadiya and Jacques D. Fleuriot and Daniel Domingo-Fern{\'a}ndez},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=KWWbS5yVtl}
} | Recently, several machine learning approaches have aided drug discovery by identifying promising candidates and predicting potential indications. However, understanding the ways in which drugs achieve their therapeutic effects, otherwise known as their mechanisms-of-action (MoA), is important for understanding potency, side effects, and interactions with various tissue types, among other things. We leveraged and improved the interpretability of a neurosymbolic reinforcement learning method in an attempt to reveal MoAs. While doing so, we observed that our findings raised several concerns with the reasoning process. Specifically, we debate situations in which patterns following a "guilt-by-association" trend are useful for predictions regarding novel compounds. We present our results to facilitate discussion about how generalizable ML-based models are to the drug discovery process as well as how important interpretability can be to such models. | Neurosymbolic AI Reveals Biases and Limitations in ML-Driven Drug Discovery | [
"Lauren DeLong",
"Yojana Gadiya",
"Jacques D. Fleuriot",
"Daniel Domingo-Fernández"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=KWIM7ZNYxb | @inproceedings{
martinelli2023leveraging,
title={Leveraging expert feedback to align proxy and ground truth rewards in goal-oriented molecular generation},
author={Julien Martinelli and Yasmine Nahal and Duong L{\^e} and Ola Engkvist and Samuel Kaski},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=KWIM7ZNYxb}
} | Reinforcement learning has proven useful for _de novo_ molecular design. Leveraging a reward function associated with a given design task allows for efficiently exploring the chemical space, thus producing relevant candidates.
Nevertheless, while tasks involving optimization of drug-likeness properties such as LogP or molecular weight do enjoy a tractable and cheap-to-evaluate reward definition, more realistic objectives such as bioactivity or binding affinity do not.
For such tasks, the ground truth reward is prohibitively expensive to compute and cannot be done inside a molecule generation loop, thus it is usually taken as the output of a statistical model.
Such a model will act as a faulty reward signal when taken out-of-training distribution, which typically happens when exploring the chemical space, thus leading to molecules judged promising by the system, but which do not align with reality.
We investigate this alignment problem through the lens of Human-In-The-Loop ML and propose a combination of two reward models independently trained on experimental data and expert feedback, with a gating process that decides which model output will be used as a reward for a given candidate. This combined system can be fine-tuned as expert feedback is acquired throughout the molecular design process, using several active learning criteria that we evaluate. In this active learning regime, our combined model demonstrates an improvement over the vanilla setting, even for noisy expert feedback. | Leveraging expert feedback to align proxy and ground truth rewards in goal-oriented molecular generation | [
"Julien Martinelli",
"Yasmine Nahal",
"Duong Lê",
"Ola Engkvist",
"Samuel Kaski"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Jjcd2SaH9B | @inproceedings{
zhou2023cryostar,
title={Cryo{STAR}: Cryo-{EM} Heterogeneous Reconstruction of Atomic Models with Structural Regularization},
author={Yi Zhou and Yilai Li and Jing Yuan and Fei YE and Quanquan Gu},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=Jjcd2SaH9B}
} | Atomic models, which directly represent molecular structural variations (i.e., conformation), have received increasing attention in the field of cryo-electron microscopy (cryo-EM) heterogeneity analysis. However, the nonconvex nature of the structural space (the space of atomic coordinates) poses a significant challenge to finding a physical-plausible solution. In this paper, we address this challenge by proposing a novel approach, named cryoSTAR, with the aim of reconstructing atomic models from cryo-EM images.
Our approach is motivated by the observation that weak regularization allows atomic models to be excessively flexible in the search space, resulting in a loss of local structural fidelity, while strong regularization tends to trap atomic models in the neighborhood of the initial structure, limiting their ability to explore the conformational landscape effectively. To strike a balance, we introduce adaptive structural regularization at the atomic level to modulate the reconstruction process. We relax the flexible region adaptively to allow for greater conformational changes. Our method achieves the lowest RMSD (up to a maximum decrease of 7.14\AA) on a synthetic dataset, and uncovers reasonable dynamics on an experimental dataset, highlighting its generalizability across different protein systems. Our work sheds light on the potential of atomic models as an alternative to traditional volumetric density maps for cryo-EM heterogeneous reconstruction. | CryoSTAR: Cryo-EM Heterogeneous Reconstruction of Atomic Models with Structural Regularization | [
"Yi Zhou",
"Yilai Li",
"Jing Yuan",
"Fei YE",
"Quanquan Gu"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=JaOXEQWqwK | @inproceedings{
badkul2023trustaffinity,
title={TrustAffinity: accurate, reliable and scalable out-of-distribution protein-ligand binding affinity prediction using trustworthy deep learning},
author={Amitesh Badkul and Li Xie and Shuo Zhang and Lei Xie},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=JaOXEQWqwK}
} | Accurate, reliable and scalable predictions of protein-ligand binding affinity have a great potential to accelerate drug discovery. Despite considerable efforts, three challenges remain: out-of-distribution (OOD) generalizations for understudied proteins or compounds from unlabeled protein families or chemical scaffolds, uncertainty quantification of individual predictions, and scalability to billions of compounds. We propose a sequence-based deep learning framework, TrustAffinity, to address aforementioned challenges. TrustAffinity synthesizes a structure-informed protein language model, efficient uncertainty quantification based on residue-estimation and novel uncertainty regularized optimization. We extensively validate TrustAffinity in multiple OOD settings. TrustAffinity significantly outperforms state-of-the-art computational methods by a large margin. It achieves a Pearson’s correlation between predicted and actual binding affinities above 0.9 with a high confidence and at least three orders of magnitude of faster than protein-ligand docking, highlighting its potential in real-world drug discovery. We further demonstrate TrustAffinity’s practicality through an Opioid Use Disorder lead discovery case study. | TrustAffinity: accurate, reliable and scalable out-of-distribution protein-ligand binding affinity prediction using trustworthy deep learning | [
"Amitesh Badkul",
"Li Xie",
"Shuo Zhang",
"Lei Xie"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=IEHtoMTOv9 | @inproceedings{
kim2023scalable,
title={Scalable Normalizing Flows Enable Boltzmann Generators for Macromolecules},
author={Joseph Chahn Kim and David A Bloore and Karan Kapoor and Jun Feng and Ming-Hong Hao and Mengdi Wang},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=IEHtoMTOv9}
} | The Boltzmann distribution of a protein provides a roadmap to all of its functional states. Normalizing flows are a promising tool for modeling this distribution, but current methods are intractable for typical pharmacological targets; they become computationally intractable due to the size of the system, heterogeneity of intra-molecular potential energy, and long-range interactions. To remedy these issues, we present a novel flow architecture that utilizes split channels and gated attention to efficiently learn the conformational distribution of proteins defined by internal coordinates. We show that by utilizing a 2-Wasserstein loss, one can smooth the transition from maximum likelihood training to energy-based training, enabling the training of Boltzmann Generators for macromolecules. We evaluate our model and training strategy on villin headpiece HP35(nle-nle), a 35-residue subdomain, and protein G, a 56-residue protein. We demonstrate that standard architectures and training strategies, such as maximum likelihood alone, fail while our novel architecture and multi-stage training strategy are able to model the conformational distributions of protein G and HP35. | Scalable Normalizing Flows Enable Boltzmann Generators for Macromolecules | [
"Joseph Chahn Kim",
"David A Bloore",
"Karan Kapoor",
"Jun Feng",
"Ming-Hong Hao",
"Mengdi Wang"
] | Workshop/AI4D3 | 2401.04246 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=GwLt3P9DMI | @inproceedings{
nori2023evaluating,
title={Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation},
author={Divya Nori and Simon V Mathis and Amir Pouya Shanehsazzadeh},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=GwLt3P9DMI}
} | The success of therapeutic antibodies relies on their ability to selectively bind antigens. AI-based antibody design protocols have shown promise in generating epitope-specific designs. Many of these protocols use an inverse folding step to generate diverse sequences given a backbone structure. Due to prohibitive screening costs, it is key to identify candidate sequences likely to bind in vitro. Here, we compare the efficacy of 8 common scoring paradigms based on open-source models to classify antibody designs as binders or non-binders. We evaluate these approaches on a novel surface plasmon resonance (SPR) dataset, spanning 5 antigens. Our results show that existing methods struggle to detect binders, and performance is highly variable across antigens. We find that metrics computed on flexibly docked antibody-antigen complexes are more robust, and ensembles scores are more consistent than individual metrics. We provide experimental insight to analyze current scoring techniques, highlighting that the development of robust, zero-shot filters is an important research gap. | Evaluating Zero-Shot Scoring for In Vitro Antibody Binding Prediction with Experimental Validation | [
"Divya Nori",
"Simon V Mathis",
"Amir Pouya Shanehsazzadeh"
] | Workshop/AI4D3 | 2312.05273 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=GGYffFQGkZ | @inproceedings{
tsepa2023congfu,
title={CongFu: Conditional Graph Fusion for Drug Synergy Prediction},
author={Oleksii Tsepa and Bohdan Naida and Anna Goldenberg and BO WANG},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=GGYffFQGkZ}
} | Drug synergy, characterized by the amplified combined effect of multiple drugs, is critically important for optimizing therapeutic outcomes. Limited data on drug synergy, arising from the vast number of possible drug combinations and testing costs, motivate the need for predictive methods. In this work, we introduce CongFu, a novel Conditional Graph Fusion Layer, designed to predict drug synergy. CongFu employs an attention mechanism and a bottleneck to extract local graph contexts and conditionally fuse graph data within a global context. Its modular architecture enables flexible replacement of layer modules, including readouts and graph encoders, facilitating customization for diverse applications. To evaluate the performance of CongFu, we conduct comprehensive experiments on four datasets, encompassing three distinct setups for drug synergy prediction. CongFu achieves state-of-the-art results on 11 out of 12 benchmark datasets, demonstrating its ability to capture intricate patterns of drug synergy. Through ablation studies, we validate the significance of individual layer components, affirming their contributions to overall predictive performance. Finally, we propose an explainability strategy for elucidating the effect of drugs on genes. By addressing the challenge of predicting drug synergy in untested drug pairs and utilizing our proposed explainability approach, CongFu opens new avenues for optimizing drug combinations and advancing personalized medicine. | CongFu: Conditional Graph Fusion for Drug Synergy Prediction | [
"Oleksii Tsepa",
"Bohdan Naida",
"Anna Goldenberg",
"BO WANG"
] | Workshop/AI4D3 | 2305.14517 | [
"https://github.com/bowang-lab/congfu"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=GEqSrmfbvv | @inproceedings{
linsley2023the,
title={The neural scaling laws for phenotypic drug discovery},
author={Drew Linsley and John Griffin and Jason Parker Brown and Adam N Roose and Michael Frank and Steven Finkbeiner and Peter S Linsley and Jeremy William Linsley},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=GEqSrmfbvv}
} | Recent breakthroughs by deep neural networks (DNNs) in natural language processing (NLP) and computer vision have been driven by a scale-up of models and data rather than the discovery of novel computing paradigms. Here, we investigate if scale can have a similar impact for models designed to aid small molecule drug discovery. We address this question through a large-scale and systematic analysis of how DNN size, data diet, and learning routines interact to impact accuracy on our Phenotypic Chemistry Arena (Pheno-CA) benchmark — a diverse set of drug development tasks posed on image-based high content screening data. Surprisingly, we find that DNNs explicitly supervised to solve tasks in the Pheno-CA do not continuously improve as their data and model size is scaled-up. To address this issue, we introduce a novel precursor task, the Inverse Biological Process (IBP), which is designed to resemble the causal objective functions that have proven successful for NLP. We indeed find that DNNs first trained with IBP then probed for performance on the Pheno-CA significantly outperform task-supervised DNNs. More importantly, the performance of these IBP-trained DNNs monotonically improves with data and model scale. Our findings reveal that the DNN ingredients needed to accurately solve small molecule drug development tasks are already in our hands, and project how much more experimental data is needed to achieve any desired level of improvement. | The neural scaling laws for phenotypic drug discovery | [
"Drew Linsley",
"John Griffin",
"Jason Parker Brown",
"Adam N Roose",
"Michael Frank",
"Steven Finkbeiner",
"Peter S Linsley",
"Jeremy William Linsley"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=G3WhQ8BtAI | @inproceedings{
sestak2023vnegnn,
title={{VN}-{EGNN}: Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification},
author={Florian Sestak and Lisa Schneckenreiter and Sepp Hochreiter and Andreas Mayr and G{\"u}nter Klambauer},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=G3WhQ8BtAI}
} | Being able to identify regions within or around proteins, to which ligands can
potentially bind, is an essential step to develop new drugs. Binding site iden-
tification methods can now profit from the availability of large amounts of 3D
structures in protein structure databases or from AlphaFold predictions. Current
binding site identification methods rely on geometric deep learning, which takes
geometric invariances and equivariances into account. Such methods turned out
to be very beneficial for physics-related tasks like binding energy or motion tra-
jectory prediction. However, their performance at binding site identification is
still limited, which might be due to limited expressivity or oversquashing effects
of E(n)-Equivariant Graph Neural Networks (EGNNs). Here, we extend EGNNs
by adding virtual nodes and applying an extended message passing scheme. The
virtual nodes in these graphs both improve the predictive performance and can also
learn to represent binding sites. In our experiments, we show that VN-EGNN sets
a new state of the art at binding site identification on three common benchmarks,
COACH420, HOLO4K, and PDBbind2020. | VN-EGNN: Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification | [
"Florian Sestak",
"Lisa Schneckenreiter",
"Sepp Hochreiter",
"Andreas Mayr",
"Günter Klambauer"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=CPzuoJAVHm | @inproceedings{
kirchoff2023salsa,
title={{SALSA}: Semantically-Aware Latent Space Autoencoder},
author={Kathryn E Kirchoff and Travis Maxfield and Alexander Tropsha and Shawn M Gomez},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=CPzuoJAVHm}
} | In learning molecular representations, SMILES strings enable the use of powerful NLP methodologies, such as sequence autoencoders. However, an autoencoder trained solely on SMILES is insufficient to learn molecular representations that are semantically meaningful, which capture structural similarities between molecules. We demonstrate by example that a standard SMILES autoencoder may map structurally similar molecules to distant latent vectors, resulting in an incoherent latent space. To address this shortcoming we propose Semantically-Aware Latent Space Autoencoder (SALSA), a transformer-autoencoder modified with a contrastive objective of mapping structurally similar molecules to nearby vectors in the latent space. We evaluate semantic awareness of SALSA representations by comparing to a naive autoencoder as well as the standard ECFP4. We show empirically that SALSA learns a representation that maintains 1) structural awareness, 2) physicochemical property awareness, 3) biological property awareness, and 4) semantic continuity. | SALSA: Semantically-Aware Latent Space Autoencoder | [
"Kathryn E Kirchoff",
"Travis Maxfield",
"Alexander Tropsha",
"Shawn M Gomez"
] | Workshop/AI4D3 | 2310.02744 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=BXC5DkKqJj | @inproceedings{
noutahi2023gotta,
title={Gotta be {SAFE}: A New Framework for Molecular Design},
author={Emmanuel Noutahi and Cristian Gabellini and Michael Craig and Jonathan Siu Chi Lim and Prudencio Tossou},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=BXC5DkKqJj}
} | Traditional molecular string representations, such as SMILES, often pose challenges for AI-driven molecular design due to their non-sequential depiction of molecular substructures. To address this issue, we introduce Sequential Attachment-based Fragment Embedding (SAFE), a novel line notation for chemical structures. SAFE reimagines SMILES strings as an unordered sequence of interconnected fragment blocks while maintaining full compatibility with existing SMILES parsers. It streamlines complex generative tasks, including scaffold decoration, fragment linking, polymer generation, and scaffold hopping, while facilitating autoregressive generation for fragment-constrained design, thereby eliminating the need for intricate decoding or graph-based models. We demonstrate the effectiveness of SAFE by training an 87-million-parameter GPT2-like model on a dataset containing 1.1 billion SAFE representations. Through extensive experimentation, we show that our SAFE-GPT model exhibits versatile and robust optimization performance. SAFE opens up new avenues for the rapid exploration of chemical space under various constraints, promising breakthroughs in AI-driven molecular design. | Gotta be SAFE: A New Framework for Molecular Design | [
"Emmanuel Noutahi",
"Cristian Gabellini",
"Michael Craig",
"Jonathan Siu Chi Lim",
"Prudencio Tossou"
] | Workshop/AI4D3 | 2310.10773 | [
"https://github.com/datamol-io/safe"
] | https://huggingface.co/papers/2310.10773 | 2 | 0 | 0 | 5 | [
"datamol-io/safe-gpt"
] | [
"datamol-io/safe-gpt",
"datamol-io/safe-drugs",
"anrilombard/safe-gpt-small"
] | [
"bcadkins01/beta_lactam_demo"
] | [
"datamol-io/safe-gpt"
] | [
"datamol-io/safe-gpt",
"datamol-io/safe-drugs",
"anrilombard/safe-gpt-small"
] | [
"bcadkins01/beta_lactam_demo"
] | 1 | poster |
null | https://openreview.net/forum?id=BDIljrhC5q | @inproceedings{
zhang2023moleculegpt,
title={Molecule{GPT}: Instruction Following Large Language Models for Molecular Property Prediction},
author={Weitong Zhang and Xiaoyun Wang and Weili Nie and Joe Eaton and Brad Rees and Quanquan Gu},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=BDIljrhC5q}
} | Harnessing textual information offers significant advantages in the drug design process, providing invaluable insights into complex molecular structures and facilitating molecule design based on textual instructions. With recent advancements in the utilization of Large Language Models (LLMs) for multi-modal data applications, we aim to leverage the capabilities of LLM for molecule property prediction tasks. We introduce MoleculeGPT, which is designed to provide answers to queries concerning molecular properties on the basis of molecular structure inputs. To train the MoleculeGPT, we have curated a new dataset from the raw molecule description in PubChem for instruction-following tasks. We evaluate the performance of MoleculeGPT on multiple-choice questions and several downstream tasks on molecule property prediction for drug design. Experimental results show that MoleculeGPT can generate responses that closely resemble human-level performance and demonstrate exceptional capabilities across diverse downstream tasks. | MoleculeGPT: Instruction Following Large Language Models for Molecular Property Prediction | [
"Weitong Zhang",
"Xiaoyun Wang",
"Weili Nie",
"Joe Eaton",
"Brad Rees",
"Quanquan Gu"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=Az6WpRVkuQ | @inproceedings{
chan2023embracing,
title={Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions},
author={Lucian Chan and Marcel Verdonk and Carl Poelking},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=Az6WpRVkuQ}
} | Predicting the bioactivity of a ligand is one of the hardest and most important challenges in computer-aided drug discovery. Despite years of data collection and curation efforts, bioactivity data remains sparse and heterogeneous, thus hampering efforts to build predictive models that are accurate, transferable and robust. The intrinsic variability of the experimental data is further compounded by data aggregation practices that neglect heterogeneity to overcome sparsity. Here we discuss the limitations of these practices and present a hierarchical meta-learning framework that exploits the information synergy across disparate assays by successfully accounting for assay heterogeneity. We show that the model achieves a drastic improvement in affinity prediction across diverse protein targets and assay types compared to conventional baselines. It can quickly adapt to new target contexts using very few observations, thus enabling large-scale virtual screening in early-phase drug discovery. | Embracing assay heterogeneity with neural processes for markedly improved bioactivity predictions | [
"Lucian Chan",
"Marcel Verdonk",
"Carl Poelking"
] | Workshop/AI4D3 | 2308.09086 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
|
null | https://openreview.net/forum?id=AlWm9Cyelk | @inproceedings{
yalabadi2023fragxsitedti,
title={FragXsite{DTI}: an interpretable transformer-based model for drug-target interaction prediction},
author={Ali Khodabandeh Yalabadi and Mehdi Yazdani-Jahromi and Niloofar Yousefi and Aida Tayebi and Sina Abdidizaji and Ozlem Garibay},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=AlWm9Cyelk}
} | Drug-Target Interaction (DTI) prediction is vital for drug discovery, yet challenges persist in achieving model interpretability and optimizing performance. We propose a novel transformer-based model, FragXsiteDTI, that aims to address these challenges in DTI prediction. Notably, FragXsiteDTI is the first DTI model to simultaneously leverage drug molecule fragments and protein pockets. Our information-rich representations for both proteins and drugs offer a detailed perspective on their interaction. Inspired by the Perceiver IO framework, our model features a learnable latent array, initially interacting with protein binding site embeddings using cross-attention and later refined through self-attention and used as a query to the drug fragments in the drug's cross-attention transformer block. This learnable query array serves as a mediator and enables seamless information translation, preserving critical nuances in drug-protein interactions. Our computational results on two benchmarking datasets demonstrate the superior predictive power of our model over several state-of-the-art models. We also show the interpretability of our model in terms of the critical components of both target proteins and drug molecules within drug-target pairs. | FragXsiteDTI: an interpretable transformer-based model for drug-target interaction prediction | [
"Ali Khodabandeh Yalabadi",
"Mehdi Yazdani-Jahromi",
"Niloofar Yousefi",
"Aida Tayebi",
"Sina Abdidizaji",
"Ozlem Garibay"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=9Zim5Lpsq3 | @inproceedings{
qiu2023tree,
title={Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization},
author={Jiahao Qiu and Hui Yuan and Jinghong Zhang and Wentao Chen and Huazheng Wang and Mengdi Wang},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=9Zim5Lpsq3}
} | While modern biotechnologies allow synthesizing new proteins and function measurements at scale, efficiently exploring a protein sequence space and engineering it remains a daunting task due to the vast sequence space of any given protein. Protein engineering is typically conducted through an iterative process of adding mutations to the wild-type or lead sequences, recombination of mutations, and running new rounds of screening. To enhance the efficiency of such a process, we propose a tree search-based bandit learning method, which expands a tree starting from the initial sequence with the guidance of a bandit machine learning model. Under simplified assumptions and a Gaussian Process prior, we provide theoretical analysis and a Bayesian regret bound, demonstrating that the method can efficiently discover a near-optimal design. The full algorithm is compatible with a suite of randomized tree search heuristics, machine learning models, pre-trained embeddings, and bandit techniques. We test various instances of the algorithm across benchmark protein datasets using simulated screens. Experiment results demonstrate that the algorithm is both sample-efficient, diversity-promoting, and able to find top designs using reasonably small mutation counts. | Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization | [
"Jiahao Qiu",
"Hui Yuan",
"Jinghong Zhang",
"Wentao Chen",
"Huazheng Wang",
"Mengdi Wang"
] | Workshop/AI4D3 | 2401.06173 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9EHoWfkAUT | @inproceedings{
amritpal2023graphprint,
title={GraphPrint: Extracting Features from 3D Protein Structure for Drug Target Affinity Prediction},
author={Amritpal},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=9EHoWfkAUT}
} | Accurate drug target affinity prediction can improve drug candidate selection, accelerate the drug discovery process, and reduce drug production costs. Previous work focused on traditional fingerprints or used features extracted based on the amino acid sequence in the protein, ignoring its 3D structure which affects its binding affinity. In this work, we propose GraphPrint: a framework for incorporating 3D protein structure features for drug target affinity prediction. We generate graph representations for protein 3D structures using amino acid residue location coordinates and combine them with drug graph representation and traditional features to jointly learn drug target affinity. Our model achieves a mean square error of 0.1378 and a concordance index of 0.8929 on the KIBA dataset and improves over using traditional protein features alone. Our ablation study shows that the 3D protein structure-based features provide information complementary to traditional features. | GraphPrint: Extracting Features from 3D Protein Structure for Drug Target Affinity Prediction | [
"Amritpal"
] | Workshop/AI4D3 | 2407.10452 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=9DxJQpzZTn | @inproceedings{
cao2023largescale,
title={Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening},
author={Zhonglin Cao and Simone Sciabola and Ye Wang},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=9DxJQpzZTn}
} | Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, brute-force virtual screening using traditional tools such as docking becomes infeasible in terms of time and computational resources. Active learning and Bayesian optimization has recently been proven as effective methods of narrowing down the search space. An essential component in those methods is a surrogate machine learning model that is trained with a small subset of the library to predict the desired properties of compounds. Accurate model can achieve high sample efficiency by finding the most promising compounds with only a fraction of the whole library being virtually screened. In this study, we examined the performance of pretrained transformer- based language model and graph neural network in Bayesian optimization active learning framework. The best pretrained models identifies 58.97% of the top-50000 by docking score after screening only 0.6% of an ultra-large library containing 99.5 million compounds, improving 8% over previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Such model can serve as a boost to the accuracy and sample efficiency of active learning based molecule virtual screening. | Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening | [
"Zhonglin Cao",
"Simone Sciabola",
"Ye Wang"
] | Workshop/AI4D3 | 2309.11687 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8ZR5YndPwu | @inproceedings{
shenoy2023role,
title={Role of Structural and Conformational Diversity for Machine Learning Potentials},
author={Nikhil Shenoy and Prudencio Tossou and Emmanuel Noutahi and Hadrien Mary and Dominique Beaini and Jiarui Ding},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=8ZR5YndPwu}
} | In the field of Machine Learning Interatomic Potentials (MLIPs), understanding the intricate relationship between data biases, specifically conformational and structural diversity, and model generalization is critical in improving the quality of Quantum Mechanics (QM) data generation efforts. We investigate these dynamics through two distinct experiments: a fixed budget one, where the dataset size remains constant, and a fixed molecular set one, which focuses on fixed structural diversity while varying conformational diversity. Our results reveal nuanced patterns in generalization metrics. Notably, for optimal structural and conformational generalization, a careful balance between structural and conformational diversity is required, but existing QM datasets do not meet that trade-off. Additionally, our results highlight the limitation of the models at generalizing beyond their training distribution, emphasizing the importance of defining applicability domain during model deployment. These findings provide valuable insights and guidelines for QM data generation efforts. | Role of Structural and Conformational Diversity for Machine Learning Potentials | [
"Nikhil Shenoy",
"Prudencio Tossou",
"Emmanuel Noutahi",
"Hadrien Mary",
"Dominique Beaini",
"Jiarui Ding"
] | Workshop/AI4D3 | 2311.00862 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=8RPbtOC6Sl | @inproceedings{
liu2023drugimprover,
title={DrugImprover: Utilizing Reinforcement Learning for Multi-Objective Alignment in Drug Optimization},
author={Xuefeng Liu and Songhao Jiang and Archit Vasan and Alexander Brace and Ozan Gokdemir and Thomas Brettin and Fangfang Xia and Ian Foster and Rick Stevens},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=8RPbtOC6Sl}
} | Reinforcement learning from human feedback (RLHF) is a method for enhancing the finetuning of large language models (LLMs), leading to notable performance improvements that can also align better with human values. Building upon the inspiration drawn from RLHF, this research delves into the realm of drug optimization. We employ reinforcement learning to finetune a drug optimization model, enhancing the original drug across multiple target objectives, while retains the beneficial chemical properties of the original drug. Our proposal comprises three primary components: (1) DRUGIMPROVER: A framework tailored for improving robustness and efficiency in drug optimization. (2) A novel Advantage-alignment Policy Optimization (APO) with multi-critic guided exploration algorithm for finetuning the objective-oriented properties. (3) A dataset of 1 million compounds, each with OEDOCK docking scores on 5 human proteins associated with cancer cells and 24 proteins from SARS-CoV-2 virus. We conduct a comprehensive evaluation of APO and demonstrate its effectiveness in improving the original drug across multiple properties. Our code and dataset are made public at: https://github.com/Argonne-National-Laboratory/DrugImprover. | DrugImprover: Utilizing Reinforcement Learning for Multi-Objective Alignment in Drug Optimization | [
"Xuefeng Liu",
"Songhao Jiang",
"Archit Vasan",
"Alexander Brace",
"Ozan Gokdemir",
"Thomas Brettin",
"Fangfang Xia",
"Ian Foster",
"Rick Stevens"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | oral |
||
null | https://openreview.net/forum?id=6KdR429i2Q | @inproceedings{
rollins2023ablef,
title={Ab{LEF}: Antibody Language Ensemble Fusion for thermodynamically empowered property predictions},
author={Zachary A Rollins and Talal Widatalla and Andrew Waight and Alan C Cheng and Essam Metwally},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=6KdR429i2Q}
} | Pre-trained protein language and/or structural models are often fine-tuned on drug development properties (i.e., developability properties) to accelerate
drug discovery initiatives. However, these models generally rely on a single structural conformation and/or a single sequence as a molecular representation.
We present a physics-based model whereby structural ensemble representations are fused by a transformer-based architecture and concatenated to
a language representation to predict antibody protein properties. AbLEF enables the direct infusion of thermodynamic information into latent space and this enhances
property prediction by explicitly infusing dynamic molecular behavior that occurs during experimental measurement.
We find that $\textbf{(1)}$ ensembles of structures generated from molecular simulation can further improve antibody property prediction for small datasets,
$\textbf{(2)}$ fine-tuned large protein language models can match smaller antibody-specific language models at predicting antibody properties,
$\textbf{(3)}$ trained multimodal sequence and structural representations outperform sequence representations alone, $\textbf{(4)}$ pre-trained sequence with
structure models are competitive with shallow machine learning (ML) methods in the small data regime, and $\textbf{(5)}$ predicting measured antibody properties remains
difficult for limited high fidelity datasets. AbLEF has been made publicly available at https://github.com/merck/AbLEF. | AbLEF: Antibody Language Ensemble Fusion for thermodynamically empowered property predictions | [
"Zachary A Rollins",
"Talal Widatalla",
"Andrew Waight",
"Alan C Cheng",
"Essam Metwally"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=5Wt7qNRc5u | @inproceedings{
plainer2023diffdockpocket,
title={DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility},
author={Michael Plainer and Marcella Toth and Simon Dobers and Hannes Stark and Gabriele Corso and C{\'e}line Marquet and Regina Barzilay},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=5Wt7qNRc5u}
} | When a small molecule binds to a protein, the 3D structure of the protein and its function change. Understanding this process, called molecular docking, can be crucial in areas such as drug design. Recent learning-based attempts have shown promising results at this task, yet lack features that traditional approaches support. In this work, we close this gap by proposing DiffDock-Pocket, a diffusion-based docking algorithm that is conditioned on a binding target to predict ligand poses only in a specific binding pocket. On top of this, our model supports receptor flexibility and predicts the position of sidechains close to the binding site. Empirically, we improve the state-of-the-art in site-specific-docking on the PDBBind benchmark. Especially when using in-silico generated structures, we achieve more than twice the performance of current methods while being more than 20 times faster than other flexible approaches. Although the model was not trained for cross-docking to different structures, it yields competitive results in this task. | DiffDock-Pocket: Diffusion for Pocket-Level Docking with Sidechain Flexibility | [
"Michael Plainer",
"Marcella Toth",
"Simon Dobers",
"Hannes Stark",
"Gabriele Corso",
"Céline Marquet",
"Regina Barzilay"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=4GwfOR6lyn | @inproceedings{
wang2023pdbstruct,
title={{PDB}-Struct: A Comprehensive Benchmark for Structure-based Protein Design},
author={Chuanrui Wang and Bozitao Zhong and Zuobai Zhang and Narendra Chaudhary and Sanchit Misra and Jian Tang},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=4GwfOR6lyn}
} | Structure-based protein design has attracted increasing interest, with numerous methods being introduced in recent years.
However, a universally accepted method for evaluation has not been established, since the wet-lab validation can be overly time-consuming for the development of new algorithms, and the $\textit{in silico}$ validation with recovery and perplexity metrics is efficient but may not precisely reflect true foldability.
To address this gap, we introduce two novel metrics: refoldability-based metric, which leverages high-accuracy protein structure prediction models as a proxy for wet lab experiments, and stability-based metric, which assesses whether models can assign high likelihoods to experimentally stable proteins.
We curate datasets from high-quality CATH protein data, high-throughput $\textit{de novo}$ designed proteins, and mega-scale experimental mutagenesis experiments,
and in doing so, present the $\textbf{PDB-Struct}$ benchmark that evaluates both recent and previously uncompared protein design methods.
Experimental results indicate that ByProt, ProteinMPNN, and ESM-IF perform exceptionally well on our benchmark, while ESM-Design and AF-Design fall short on the refoldability metric.
We also show that while some methods exhibit high sequence recovery, they do not perform as well on our new benchmark.
Our proposed benchmark paves the way for a fair and comprehensive evaluation of protein design methods in the future. Code is available at https://github.com/WANG-CR/PDB-Struct. | PDB-Struct: A Comprehensive Benchmark for Structure-based Protein Design | [
"Chuanrui Wang",
"Bozitao Zhong",
"Zuobai Zhang",
"Narendra Chaudhary",
"Sanchit Misra",
"Jian Tang"
] | Workshop/AI4D3 | 2312.00080 | [
"https://github.com/wang-cr/pdb-struct"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=3mRveUdfXA | @inproceedings{
bloore2023protein,
title={Protein Language Models Enable Accurate Cryptic Ligand Binding Pocket Prediction},
author={David A Bloore and Joseph Chahn Kim and Karan Kapoor and Eric Chen and Kaifu Gao and Mengdi Wang and Ming-Hong Hao},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=3mRveUdfXA}
} | Accurate prediction of protein-ligand binding pockets is a critical task in protein functional analysis and small molecule pharmaceutical design. However, the flexible and dynamic nature of proteins conceal an unknown number of potentially invaluable "cryptic" pockets. Current approaches for cryptic pocket discovery rely on molecular dynamics (MD), leading to poor scalability and bias. Even recent ML-based cryptic pocket discovery approaches require large, post-processed MD datasets to train their models. In contrast, this work presents ``Efficient Sequence-based cryptic Pocket prediction'' (ESP) leveraging advanced Protein Language Models (PLMs), and demonstrates significant improvement in predictive efficacy compared to ML-based cryptic pocket prediction SOTA (ROCAUC 0.93 vs 0.87). ESP achieves detection of cryptic pockets via training on readily available, non cryptic-pocket-specific data from the PDBBind dataset, rather than costly simulation and post-processing. Further, while SOTA's predictions often include positive signal broadly distributed over a target structure, ESP produces more spatially-focused predictions which increase downstream utility. | Protein Language Models Enable Accurate Cryptic Ligand Binding Pocket Prediction | [
"David A Bloore",
"Joseph Chahn Kim",
"Karan Kapoor",
"Eric Chen",
"Kaifu Gao",
"Mengdi Wang",
"Ming-Hong Hao"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3RKshM630V | @inproceedings{
cavazos2023explaining,
title={Explaining Drug Repositioning: A Case-Based Reasoning Graph Neural Network Approach},
author={Adriana Carolina Gonzalez Cavazos and Roger Tu and Meghamala Sinha and Andrew Su},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=3RKshM630V}
} | Drug repositioning, the identification of novel uses of existing therapies, has become an attractive strategy to accelerate drug development. Knowledge graphs (KGs) have emerged as a powerful representation of interconnected data within the biomedical domain. While link prediction on biomedical can ascertain new connections between drugs and diseases, most approaches only state whether two nodes are related. Yet, they fail to explain why two nodes are related. In this project, we introduce an implementation of the semi-parametric Case-Based Reasoning over subgraphs (CBR-SUBG), designed to derive a drug query’s underlying mechanisms by gathering graph patterns of similar nodes. We show that our adaptation outperforms existing KG link prediction models on a drug repositioning task. Furthermore, our findings demonstrate that CBR-SUBG strategy can provide interpretable biological paths as evidence supporting putative repositioning candidates, leading to more informed decisions. | Explaining Drug Repositioning: A Case-Based Reasoning Graph Neural Network Approach | [
"Adriana Carolina Gonzalez Cavazos",
"Roger Tu",
"Meghamala Sinha",
"Andrew Su"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=3OW9rqKGKy | @inproceedings{
wang-henderson2023graph,
title={Graph Neural Bayesian Optimization for Virtual Screening},
author={Miles Wang-Henderson and Bartu Soyuer and Parnian Kassraie and Andreas Krause and Ilija Bogunovic},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=3OW9rqKGKy}
} | Virtual screening is an essential component of early-stage drug and materials discovery. This is challenged by the increasingly intractable size of virtual libraries and the high cost of evaluating properties. We propose GNN-SS, a Graph Neural Network (GNN) powered Bayesian Optimization (BO) algorithm. GNN-SS utilizes random sub-sampling to reduce the computational complexity of the BO problem, and diversifies queries for training the model. We further introduce data-independent projections to efficiently model second-order random feature interactions, and improve uncertainty estimates. GNN-SS is computationally light, sample-efficient, and rapidly narrows the search space by leveraging the generalization ability of GNNs. Our algorithm achieves state-of-the-art performance among screening methods for the Practical Molecular Optimization benchmark. | Graph Neural Bayesian Optimization for Virtual Screening | [
"Miles Wang-Henderson",
"Bartu Soyuer",
"Parnian Kassraie",
"Andreas Krause",
"Ilija Bogunovic"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=2UsUDE4hGw | @inproceedings{
chen2023compositional,
title={Compositional Deep Probabilistic Models of {DNA} Encoded Libraries},
author={Benson Chen and Mohammad Sultan and Theofanis Karaletsos},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=2UsUDE4hGw}
} | DNA-Encoded Library (DEL) has proven to be a powerful tool that utilizes combinatorially constructed small molecules to facilitate highly efficient screening assays. These selection experiments, involving multiple stages of washing, elution, and identification of potent binders via unique DNA barcodes, often generate complex data. This complexity can potentially mask the underlying signals, necessitating the application of computational tools such as machine learning to uncover valuable insights. We introduce a compositional deep probabilistic model of DEL data, DEL-Compose, which decomposes molecular representations into their mono-synthon, di-synthon, and tri-synthon building blocks and capitalizes on the inherent hierarchical structure of these molecules by modeling latent reactions between embedded synthons. Additionally, we investigate methods to improve the observation models for DEL count data such as integrating covariate factors to more effectively account for data noise. Across two popular public benchmark datasets (CA-IX and HRP), our model demonstrates strong performance compared to count baselines, enriches the correct pharmacophores, and offers valuable insights via its intrinsic interpretable structure, thereby providing a robust tool for the analysis of DEL data. | Compositional Deep Probabilistic Models of DNA Encoded Libraries | [
"Benson Chen",
"Mohammad Sultan",
"Theofanis Karaletsos"
] | Workshop/AI4D3 | 2310.13769 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=0YUQoQKfGC | @inproceedings{
jing2023learning,
title={Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms},
author={Bowen Jing and Tommi Jaakkola and Bonnie Berger},
booktitle={NeurIPS 2023 Workshop on New Frontiers of AI for Drug Discovery and Development},
year={2023},
url={https://openreview.net/forum?id=0YUQoQKfGC}
} | Molecular docking is critical to structure-based virtual screening, yet the throughput of such workflows is limited by the expensive optimization of scoring functions involved in most docking algorithms. We explore how machine learning can accelerate this process by learning a scoring function with a functional form that allows for more rapid optimization. Specifically, we define the scoring function to be the cross-correlation of multi-channel ligand and protein scalar fields parameterized by equivariant graph neural networks, enabling rapid optimization over rigid-body degrees of freedom with fast Fourier transforms. Moreover, the runtime of our approach can be amortized at several levels of abstraction, and is particularly favorable for virtual screening settings with a common binding pocket. We benchmark our scoring functions on two simplified docking-related tasks: decoy pose scoring and rigid conformer docking. Our method attains similar but faster performance on crystal structures compared to the Vina and Gnina scoring functions, and is more robust on computationally predicted structures. | Learning Scalar Fields for Molecular Docking with Fast Fourier Transforms | [
"Bowen Jing",
"Tommi Jaakkola",
"Bonnie Berger"
] | Workshop/AI4D3 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=zjfGRftMPw | @inproceedings{
h{\"o}ftmann2023backward,
title={Backward Learning for Goal-Conditioned Policies},
author={Marc H{\"o}ftmann and Jan Robine and Stefan Harmeling},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=zjfGRftMPw}
} | Can we learn policies in reinforcement learning without rewards? Can we learn a policy just by trying to reach a goal state? We answer these questions positively by proposing a multi-step procedure that first learns a world model that goes backward in time, secondly generates goal-reaching backward trajectories, thirdly improves those sequences using shortest path finding algorithms, and finally trains a neural network policy by imitation learning. We evaluate our method on a deterministic maze environment where the observations are $64\times 64$ pixel bird's eye images and can show that it consistently reaches several goals. | Backward Learning for Goal-Conditioned Policies | [
"Marc Höftmann",
"Jan Robine",
"Stefan Harmeling"
] | Workshop/GCRL | 2312.05044 | [
"https://github.com/hauf3n/backward-learning-for-goal-conditioned-policies"
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=zhCq4KQsYS | @inproceedings{
kim2023numerical,
title={Numerical Goal-based Transformers for Practical Conditions},
author={Seonghyun Kim and Samyeul Noh and Ingook Jang},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=zhCq4KQsYS}
} | Goal-conditioned reinforcement learning (GCRL) studies aim to apply trained agents in realistic environments. In particular, offline reinforcement learning is being studied as a way to reduce the cost of online interactions in GCRL. One such method is Decision Transformer (DT), which utilizes a numerical goal called "return-to-go" for superior performance. Since DT assumes an idealized environment, such as perfect knowledge of rewards, it is necessary to study an improved approach for real-world applications. In this work, we present various attempts and results for numerical goal-based transformers to operate under practical conditions. | Numerical Goal-based Transformers for Practical Conditions | [
"Seonghyun Kim",
"Samyeul Noh",
"Ingook Jang"
] | Workshop/GCRL | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=wQuszp2l3U | @inproceedings{
bandyopadhyay2023goalconditioned,
title={Goal-Conditioned Recommendations of {AI} Explanations},
author={Saptarashmi Bandyopadhyay and Vibhu Agrawal and Sarah Savidge and Eric Krokos and John P Dickerson},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=wQuszp2l3U}
} | The large-scale usage of Artificial Intelligence (AI) models has made it important to explain their outputs subject to requirements and goals for using these models. The definition of goals in Goal-conditioned Reinforcement Learning (GCRL) aligns with the task of recommending an appropriate explanation among Explainable AI (XAI) models like SHAP or LIME that is most interpretive for specific AI models. We focus on two goals of training random forest classifier to classify different training data in order to find appropriate explanations. SlateQ recommendation system is used for simulation where the underlying RecSim environment has a slate of documents with different quantity scores representing different goals. | Goal-Conditioned Recommendations of AI Explanations | [
"Saptarashmi Bandyopadhyay",
"Vibhu Agrawal",
"Sarah Savidge",
"Eric Krokos",
"John P Dickerson"
] | Workshop/GCRL | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=qveQJGFUjh | @inproceedings{
zheng2023contrastive,
title={Contrastive Difference Predictive Coding},
author={Chongyi Zheng and Ruslan Salakhutdinov and Benjamin Eysenbach},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=qveQJGFUjh}
} | Predicting and reasoning about the future lie at the heart of many time-series questions. For example, goal-conditioned reinforcement learning can be viewed as learning representations to predict which states are likely to be visited in the future. While prior methods have used contrastive predictive coding to model time series data, learning representations that encode long-term dependencies usually requires large amounts of data. In this paper, we introduce a temporal difference version of contrastive predictive coding that stitches together pieces of different time series data to decrease the amount of data required to learn predictions of future events. We apply this representation learning method to derive an off-policy algorithm for goal-conditioned RL. Experiments demonstrate that, compared with prior RL methods, ours achieves $2 \times$ median improvement in success rates and can better cope with stochastic environments. In tabular settings, we show that our method is about $20\times$ more sample efficient than the successor representation and $1500 \times$ more sample efficient than the standard (Monte Carlo) version of contrastive predictive coding. | Contrastive Difference Predictive Coding | [
"Chongyi Zheng",
"Ruslan Salakhutdinov",
"Benjamin Eysenbach"
] | Workshop/GCRL | 2310.20141 | [
"https://github.com/chongyi-zheng/td_infonce"
] | https://huggingface.co/papers/2310.20141 | 1 | 0 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=qU6tZmppN7 | @inproceedings{
wang2023goplan,
title={{GOP}lan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models},
author={Mianchu Wang and Rui Yang and Xi Chen and Meng Fang},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=qU6tZmppN7}
} | Offline goal-conditioned RL (GCRL) offers a feasible paradigm to learn general-purpose policies from diverse and multi-task offline datasets. Despite notable recent progress, the predominant offline GCRL methods have been restricted to model-free approaches, constraining their capacity to tackle limited data budgets and unseen goal generalization. In this work, we propose a novel two-stage model-based framework, Goal-conditioned Offline Planning (GOPlan), including (1) pretraining a prior policy capable of capturing multi-modal action distribution within the multi-goal dataset; (2) employing the reanalysis method with planning to generate imagined trajectories for funetuning policies. Specifically, the prior policy is based on an advantage-weighted Conditioned Generative Adversarial Networks that exhibits distinct mode separation to overcome the pitfalls of out-of-distribution (OOD) actions. For further policy optimization, the reanalysis method generates high-quality imaginary data by planning with learned models for both intra-trajectory and inter-trajectory goals. Through experimental evaluations, we demonstrate that GOPlan achieves state-of-the-art performance on various offline multi-goal manipulation tasks. Moreover, our results highlight the superior ability of GOPlan to handle small data budgets and generalize to OOD goals. | GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models | [
"Mianchu Wang",
"Rui Yang",
"Xi Chen",
"Meng Fang"
] | Workshop/GCRL | 2310.20025 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=l8iK6YqgkR | @inproceedings{
eberhard2023middlemile,
title={Middle-Mile Logistics Through the Lens of Goal-Conditioned Reinforcement Learning},
author={Onno Eberhard and Thibaut Cuvelier and Michal Valko and Bruno Adrien De Backer},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=l8iK6YqgkR}
} | Middle-mile logistics describes the problem of routing parcels through a network of hubs, which are linked by a fixed set of trucks. The main challenge comes from the finite capacity of the trucks. The decision to allocate a parcel to a specific truck might block another parcel from using the same truck. It is thus necessary to solve for all parcel routes simultaneously. Exact solution methods scale poorly with the problem size and real-world instances are intractable. Instead, we turn to reinforcement learning (RL) by rephrasing the middle-mile problem as a multi-object goal-conditioned Markov decision process. The key ingredients of our proposed method for parcel routing are the extraction of small feature graphs from the environment state and the combination of graph neural networks with model-free RL. There remain several open challenges and we provide an open-source implementation of the environment to encourage stronger cooperation between the reinforcement learning and logistics communities. | Middle-Mile Logistics Through the Lens of Goal-Conditioned Reinforcement Learning | [
"Onno Eberhard",
"Thibaut Cuvelier",
"Michal Valko",
"Bruno Adrien De Backer"
] | Workshop/GCRL | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ktyvmR8btd | @inproceedings{
yalcinkaya2023automata,
title={Automata Conditioned Reinforcement Learning with Experience Replay},
author={Beyazit Yalcinkaya and Niklas Lauffer and Marcell Vazquez-Chanlatte and Sanjit Seshia},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=ktyvmR8btd}
} | We explore the problem of goal-conditioned reinforcement learning (RL) where goals are represented using deterministic finite state automata (DFAs). Due to the sparse and binary nature of automata-based goals, we hypothesize that experience replay can help an RL agent learn more quickly and consistently in this setting. To enable the use of experience replay, we introduce a novel end-to-end neural architecture, including a graph neural network (GNN) to encode the DFA goal before passing it to a feed-forward policy network. Experimental results in a gridworld domain demonstrate the efficacy of the model architecture and highlight the significant role of experience replay in enhancing the learning speed and reducing the variance of RL agents for DFA tasks. | Automata Conditioned Reinforcement Learning with Experience Replay | [
"Beyazit Yalcinkaya",
"Niklas Lauffer",
"Marcell Vazquez-Chanlatte",
"Sanjit Seshia"
] | Workshop/GCRL | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=ihbEwhPaXe | @inproceedings{
rahman2023empowering,
title={Empowering Clinicians with Me{DT}: A Framework for Sepsis Treatment},
author={Aamer Abdul Rahman and Pranav Agarwal and Vincent Michalski and Rita Noumeir and Samira Kahou},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=ihbEwhPaXe}
} | Offline reinforcement learning has shown promise for solving tasks in safety-critical settings, such as clinical decision support. Its application, however, has been limited by the need for interpretability and interactivity for clinicians. To address these challenges, we propose medical decision transformer (MeDT), a novel and versatile framework based on the goal-conditioned reinforcement learning (RL) paradigm for sepsis treatment recommendation. MeDT is based on the decision transformer architecture, and conditions the model on expected treatment outcomes, hindsight patient acuity scores, past dosages and the patient’s current and past medical state
at every timestep. This allows it to consider the complete context of a patient’s medical history, enabling more informed decision-making. By conditioning the policy’s generation of actions on user-specified goals at every timestep, MeDT enables clinician interactability while avoiding the problem of sparse rewards. Using data from the MIMIC-III dataset, we show that MeDT produces interventions that outperform
or are competitive with existing methods while enabling a more interpretable, personalized and clinician-directed approach. For future research, we release our code at https://aamer98.github.io/medical_decision_transformer/. | Empowering Clinicians with MeDT: A Framework for Sepsis Treatment | [
"Aamer Abdul Rahman",
"Pranav Agarwal",
"Vincent Michalski",
"Rita Noumeir",
"Samira Kahou"
] | Workshop/GCRL | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=hw0WiUnS0G | @inproceedings{
mccallum2023is,
title={Is feedback all you need? Leveraging natural language feedback in goal-conditioned {RL}},
author={Sabrina McCallum and Max Taylor-Davies and Stefano Albrecht and Alessandro Suglia},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=hw0WiUnS0G}
} | Despite numerous successes, the field of reinforcement learning (RL) remains far from matching the impressive generalisation power of human behaviour learning. One way to help bridge this gap may be to provide RL agents with richer, more human-like feedback expressed in natural language. First, we extend BabyAI to automatically generate language feedback from the environment dynamics and goal condition success. Then, we modify the Decision Transformer architecture to take advantage of this additional signal. We find that training with language feedback either in place of or in addition to the return-to-go or goal descriptions improves agents’ generalisation performance, and that agents can benefit from feedback even when this is only available during training, but not at inference. | Is feedback all you need? Leveraging natural language feedback in goal-conditioned RL | [
"Sabrina McCallum",
"Max Taylor-Davies",
"Stefano Albrecht",
"Alessandro Suglia"
] | Workshop/GCRL | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=gOGGTsTliS | @inproceedings{
zhang2023universal,
title={Universal Visual Decomposer: Long-Horizon Manipulation Made Easy},
author={Zichen Zhang and Yunshuang Li and Osbert Bastani and Abhishek Gupta and Dinesh Jayaraman and Yecheng Jason Ma and Luca Weihs},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=gOGGTsTliS}
} | Real-world robotic tasks stretch over extended horizons and encompass multiple stages. Learning long-horizon manipulation tasks, however, is a long-standing challenge, and demands decomposing the overarching task into several manageable subtasks to facilitate policy learning and generalization to unseen tasks. Prior task decomposition methods require task-specific knowledge, are computationally intensive, and cannot readily be applied to new tasks. To address these shortcomings, we propose Universal Visual Decomposer (UVD), an off-the-shelf task decomposition method for visual long-horizon manipulation using pre-trained visual representations designed for robotic control. At a high level, UVD discovers subgoals by detecting phase shifts in the embedding space of the pre-trained representation. Operating purely on visual demonstrations without auxiliary information, UVD can effectively extract visual subgoals embedded in the videos, while incurring zero additional training cost on top of standard visuomotor policy training. Goal-conditioned policies learned with UVD-discovered subgoals exhibit significantly improved compositional generalization at test time to unseen tasks. Furthermore, UVD-discovered subgoals can be used to construct goal-based reward shaping that jump-starts temporally extended exploration for reinforcement learning. We extensively evaluate UVD on both simulation and real-world tasks, and in all cases, UVD substantially outperforms baselines across imitation and reinforcement learning settings on in-domain and out-of-domain task sequences alike, validating the clear advantage of automated visual task decomposition within the simple, compact UVD framework. | Universal Visual Decomposer: Long-Horizon Manipulation Made Easy | [
"Zichen Zhang",
"Yunshuang Li",
"Osbert Bastani",
"Abhishek Gupta",
"Dinesh Jayaraman",
"Yecheng Jason Ma",
"Luca Weihs"
] | Workshop/GCRL | 2310.08581 | [
""
] | https://huggingface.co/papers/2310.08581 | 2 | 1 | 0 | 7 | [] | [
"zcczhang/UVD"
] | [] | [] | [
"zcczhang/UVD"
] | [] | 1 | poster |
null | https://openreview.net/forum?id=dmScrQ7SFo | @inproceedings{
sikchi2023scoremodels,
title={Score-Models for Offline Goal-Conditioned Reinforcement Learning},
author={Harshit Sikchi and Rohan Chitnis and Ahmed Touati and Alborz Geramifard and Amy Zhang and Scott Niekum},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=dmScrQ7SFo}
} | Offline Goal-Conditioned Reinforcement Learning (GCRL) is tasked with learning to achieve multiple goals in an environment purely from offline datasets using sparse reward functions. Offline GCRL is pivotal for developing generalist agents capable of leveraging pre-existing datasets to learn diverse and reusable skills without hand-engineering reward functions. However, contemporary approaches to GCRL based on supervised learning and contrastive learning are often suboptimal in the offline setting. An alternative perspective on GCRL optimizes for occupancy matching, but necessitates learning a discriminator, which subsequently serves as a pseudo-reward for downstream RL. Inaccuracies in the learned discriminator can cascade, negatively influencing the resulting policy. We present a novel approach to GCRL under a new lens of mixture-distribution matching, leading to our discriminator-free method: SMORe. The key insight is combining the occupancy matching perspective of GCRL with a convex dual formulation to derive a learning objective that can better leverage suboptimal offline data. SMORe learns *scores* or unnormalized densities representing the importance of taking an action at a state for reaching a particular goal. SMORe is principled and our extensive experiments on the fully offline GCRL benchmark comprised of robot manipulation and locomotion tasks, including high-dimensional observations, show that SMORe can outperform state-of-the-art baselines by a significant margin. | Score-Models for Offline Goal-Conditioned Reinforcement Learning | [
"Harshit Sikchi",
"Rohan Chitnis",
"Ahmed Touati",
"Alborz Geramifard",
"Amy Zhang",
"Scott Niekum"
] | Workshop/GCRL | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=dJvx3VtwUl | @inproceedings{
haramati2023entitycentric,
title={Entity-Centric Reinforcement Learning for Object Manipulation from Pixels},
author={Dan Haramati and Tal Daniel and Aviv Tamar},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=dJvx3VtwUl}
} | Manipulating objects is a hallmark of human intelligence, and an important task in domains such as robotics. In principle, Reinforcement Learning (RL) offers a general approach to learn object manipulation. In practice, however, domains with more than a few objects are difficult for RL agents due to the curse of dimensionality, especially when learning from raw image observations. In this work we propose a structured approach for visual RL that is suitable for representing multiple objects and their interaction, and use it to learn goal-conditioned manipulation of several objects. Key to our method is the ability to handle goals with dependencies between the objects (e.g., moving objects in a certain order). We further relate our architecture to the generalization capability of the trained agent, and demonstrate agents that learn with 3 objects but generalize to similar tasks with over 10 objects. Rollout videos are available on our website: https://sites.google.com/view/entity-centric-rl | Entity-Centric Reinforcement Learning for Object Manipulation from Pixels | [
"Dan Haramati",
"Tal Daniel",
"Aviv Tamar"
] | Workshop/GCRL | 2404.01220 | [
"https://github.com/danhrmti/ecrl"
] | https://huggingface.co/papers/2404.01220 | 0 | 0 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=cO5d5JZhx0 | @inproceedings{
badrinath2023waypoint,
title={Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets},
author={Anirudhan Badrinath and Allen Nie and Yannis Flet-Berliac and Emma Brunskill},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=cO5d5JZhx0}
} | Despite the recent advancements in offline reinforcement learning via supervised learning (RvS) methods and the success of the decision transformer (DT) architecture in various domains, DTs have proven to fall short in challenging benchmarks. The root cause of this underperformance lies in their inability to seamlessly connect segments of suboptimal trajectories, i.e., stitch, leading to poor performance. To overcome these limitations, we present a novel approach to enhance RvS methods by integrating intermediate targets. We introduce the waypoint transformer (WT), using an architecture that builds upon the DT framework and is further conditioned on dynamically-generated waypoints. The results show a significant improvement in the final return compared to existing RvS methods, with performance on par or greater than existing temporal difference learning-based methods. Additionally, the performance and stability is significantly improved
in the most challenging environments and data configurations, including AntMaze Large Play/Diverse and Kitchen Mixed/Partial. | Waypoint Transformer: Reinforcement Learning via Supervised Learning with Intermediate Targets | [
"Anirudhan Badrinath",
"Allen Nie",
"Yannis Flet-Berliac",
"Emma Brunskill"
] | Workshop/GCRL | 2306.14069 | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
|
null | https://openreview.net/forum?id=YgZNmDqyR6 | @inproceedings{
park2023metra,
title={{METRA}: Scalable Unsupervised {RL} with Metric-Aware Abstraction},
author={Seohong Park and Oleh Rybkin and Sergey Levine},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=YgZNmDqyR6}
} | Unsupervised pre-training strategies have proven to be highly effective in natural language processing and computer vision. Likewise, unsupervised reinforcement learning (RL) holds the promise of discovering a variety of potentially useful behaviors that can accelerate the learning of a wide array of downstream tasks. Previous unsupervised RL approaches have mainly focused on pure exploration and mutual information skill learning. However, despite the previous attempts, making unsupervised RL truly scalable still remains a major open challenge: pure exploration approaches might struggle in complex environments with large state spaces, where covering every possible transition is infeasible, and mutual information skill learning approaches might completely fail to explore the environment due to the lack of incentives. To make unsupervised RL scalable to complex, high-dimensional environments, we propose a novel unsupervised RL objective, which we call **Metric-Aware Abstraction** (**METRA**). Our main idea is, instead of directly covering the state space, to only cover a compact latent space $\mathcal{Z}$ that is *metrically* connected to the state space $\mathcal{S}$ by temporal distances. By learning to move in every direction in the latent space, METRA obtains a tractable set of diverse behaviors that approximately cover the state space, being scalable to high-dimensional environments. Through our experiments in five locomotion and manipulation environments, we demonstrate that METRA can discover a variety of useful behaviors even in complex, pixel-based environments, being the *first* unsupervised RL method that discovers diverse locomotion behaviors in pixel-based Quadruped and Humanoid. Our code and video are available at https://sites.google.com/view/metra0 | METRA: Scalable Unsupervised RL with Metric-Aware Abstraction | [
"Seohong Park",
"Oleh Rybkin",
"Sergey Levine"
] | Workshop/GCRL | 2310.08887 | [
"https://github.com/seohongpark/metra"
] | https://huggingface.co/papers/2310.08887 | 1 | 0 | 0 | 3 | [] | [] | [] | [] | [] | [] | 1 | poster |
null | https://openreview.net/forum?id=UyNjQ3UKO2 | @inproceedings{
pi{\k{e}}kos2023efficient,
title={Efficient Value Propagation with the Compositional Optimality Equation},
author={Piotr Pi{\k{e}}kos and Aditya Ramesh and Francesco Faccio and J{\"u}rgen Schmidhuber},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=UyNjQ3UKO2}
} | Goal-Conditioned Reinforcement Learning (GCRL) is about learning to reach predefined goal states. GCRL in the real world is crucial for adaptive robotics. Existing GCRL methods, however, suffer from low sample efficiency and high cost of collecting real-world data. Here we introduce the Compositional Optimality Equation (COE) for a widely used class of deterministic environments in which the reward is obtained only upon reaching a goal state. COE represents a novel alternative to the standard Bellman Optimality Equation, leading to more sample-efficient update rules. The Bellman update combines the immediate reward and the bootstrapped estimate of the best next state. Our COE-based update rule, however, combines the best composition of two bootstrapped estimates reflecting an arbitrary intermediate subgoal state. In tabular settings, the new update rule guarantees convergence to the optimal value function exponentially faster than the Bellman update! COE can also be used to derive compositional variants of conventional (deep) RL. In particular, our COE-based version of DDPG is more sample-efficient than DDPG in a continuous grid world. | Efficient Value Propagation with the Compositional Optimality Equation | [
"Piotr Piękos",
"Aditya Ramesh",
"Francesco Faccio",
"Jürgen Schmidhuber"
] | Workshop/GCRL | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
||
null | https://openreview.net/forum?id=UCGIQaP1mr | @inproceedings{
lifshitz2023steve,
title={{STEVE}-1: A Generative Model for Text-to-Behavior in Minecraft (Abridged Version)},
author={Shalev Lifshitz and Keiran Paster and Harris Chan and Jimmy Ba and Sheila McIlraith},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=UCGIQaP1mr}
} | Constructing AI models that respond to text instructions is challenging, especially for sequential decision-making tasks. This work introduces an instruction-tuned Video Pretraining (VPT) model for Minecraft called STEVE-1, demonstrating that the unCLIP approach, utilized in DALL•E 2, is also effective for creating instruction-following sequential decision-making agents. By leveraging pretrained models like VPT and MineCLIP and employing best practices from text-conditioned image generation, STEVE-1 costs just $60 to train and can follow a wide range of short-horizon open-ended text and visual instructions in Minecraft. STEVE-1 sets a new bar for open-ended instruction following in Minecraft with low-level controls (mouse and keyboard) and raw pixel inputs, far outperforming previous baselines. All resources, including our model weights, training scripts, and evaluation tools are made available for further research. | STEVE-1: A Generative Model for Text-to-Behavior in Minecraft (Abridged Version) | [
"Shalev Lifshitz",
"Keiran Paster",
"Harris Chan",
"Jimmy Ba",
"Sheila McIlraith"
] | Workshop/GCRL | [
""
] | -1 | -1 | -1 | -1 | [] | [] | [] | [] | [] | [] | 0 | poster |
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