\begin{algorithm}[!htbp]
	\caption{Algorithm for CPI Prediction (Multimodal)}
	\label{algo:1}
	\begin{algorithmic}[1]
		\Require Compound Graphs $\{\mathcal{G}_C^{(j)}\}_{j=1}^M$, Protein Sequences $\{S^{(i)}\}_{i=1}^N$, and Protein Graphs $\{\mathcal{G}_{\mathcal{P}}^{(i)}\}_{i=1}^N$.
  	\Ensure Interaction pattern $\mathbf{P}^{\text{cont}}$ and strength $y^{\text{aff}}$.

        \State \# \textit{Pre-training}
         \While{\textit{not convergence}}
            \State Generate a set of subsequences and subgraphs by ${\color[rgb]{1.0, 1.0, 1.0}AA}$ length-variable augmentation;
            \State Pass the augmented subsequences and subgraphs  ${\color[rgb]{1.0, 1.0, 1.0}AA}$ through the sequence and structure encoders;
            \State Pre-train the two encoders by intra-modality and ${\color[rgb]{1.0, 1.0, 1.0}AA}$  cross-modality contrastive losses in Eq.~(\ref{eq:7})(\ref{eq:8})(\ref{eq:9}).
         \EndWhile

        \State \# \textit{Fine-tuning}
        \While{\textit{not convergence}}
		\State Jointly train the two pre-trained encoders and com- ${\color[rgb]{1.0, 1.0, 1.0}AA}$ pound encoder by loss in Eq.~(\ref{eq:10}) or (\ref{eq:11}).
        \EndWhile

        \State \# \textit{Inference}
        \State Predict interaction pattern $\mathbf{P}^{\text{cont}}$ and interaction strength $y^{\text{aff}}$ for any given compound-protein pair.
		\State \textbf{return}  Interaction pattern $\mathbf{P}^{\text{cont}}$ and strength $y^{\text{aff}}$.
	\end{algorithmic}
\end{algorithm}

\begin{algorithm}
[!htbp]
\caption{Algorithm for CPI Prediction (Multimodal)}
\begin{algorithmic}
[1]
\Require Compound Graphs $\{\mathcal{G}_C^{(j)}\}_{j=1}^M$, Protein Sequences $\{S^{(i)}\}_{i=1}^N$, and Protein Graphs $\{\mathcal{G}_{\mathcal{P}}^{(i)}\}_{i=1}^N$.
\Ensure Interaction pattern $\mathbf{P}^{\text{cont}}$ and strength $y^{\text{aff}}$.
\State \# \textit{Pre-training}
\While{\textit{not convergence}}
\State Generate a set of subsequences and subgraphs by ${\color[rgb]{1.0, 1.0, 1.0}AA}$ length-variable augmentation;
\State Pass the augmented subsequences and subgraphs ${\color[rgb]{1.0, 1.0, 1.0}AA}$ through the sequence and structure encoders;
\State Pre-train the two encoders by intra-modality and ${\color[rgb]{1.0, 1.0, 1.0}AA}$ cross-modality contrastive losses in Eq.~(\ref{eq:7})(\ref{eq:8})(\ref{eq:9}).
\EndWhile
\State \# \textit{Fine-tuning}
\While{\textit{not convergence}}
\State Jointly train the two pre-trained encoders and com- ${\color[rgb]{1.0, 1.0, 1.0}AA}$ pound encoder by loss in Eq.~(\ref{eq:10}) or (\ref{eq:11}).
\EndWhile
\State \# \textit{Inference}
\State Predict interaction pattern $\mathbf{P}^{\text{cont}}$ and interaction strength $y^{\text{aff}}$ for any given compound-protein pair.
\State \textbf{return} Interaction pattern $\mathbf{P}^{\text{cont}}$ and strength $y^{\text{aff}}$.
\end{algorithmic}
\end{algorithm}
{
    "title": "psc-cpi: multi-scale protein sequence-structure contrasting for   efficient and generalizable compound-protein interaction prediction",
    "id": "2402.08198",
    "abstract": "compound-protein interaction (cpi) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. existing deep learning-based methods utilize only the single modality of protein sequences or structures and lack the co-modeling of the joint distribution of the two modalities, which may lead to significant performance drops in complex real-world scenarios due to various factors, e.g., modality missing and domain shifting. more importantly, these methods only model protein sequences and structures at a single fixed scale, neglecting more fine-grained multi-scale information, such as those embedded in key protein fragments. in this paper, we propose a novel multi-scale protein sequence-structure contrasting framework for cpi prediction (psc-cpi), which captures the dependencies between protein sequences and structures through both intra-modality and cross-modality contrasting. we further apply length-variable protein augmentation to allow contrasting to be performed at different scales, from the amino acid level to the sequence level. finally, in order to more fairly evaluate the model generalizability, we split the test data into four settings based on whether compounds and proteins have been observed during the training stage. extensive experiments have shown that psc-cpi generalizes well in all four settings, particularly in the more challenging ``unseen-both\" setting, where neither compounds nor proteins have been observed during training. furthermore, even when encountering a situation of modality missing, i.e., inference with only single-modality protein data, psc-cpi still exhibits comparable or even better performance than previous approaches.",
    "categories": "q-bio.bm cs.ai cs.lg",
    "doi": "",
    "created": "2024-02-12",
    "updated": "",
    "authors": [
        "lirong wu",
        "yufei huang",
        "cheng tan",
        "zhangyang gao",
        "bozhen hu",
        "haitao lin",
        "zicheng liu",
        "stan z. li"
    ],
    "affiliation": [],
    "url": "https://arxiv.org/abs/2402.08198"
}
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