Title: Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction,
Abstract: With access to large datasets, deep neural networks (DNN) have achieved
human-level accuracy in image and speech recognition tasks. However, in
chemistry, data is inherently small and fragmented. In this work, we develop an
approach of using rule-based knowledge for training ChemNet, a transferable and
generalizable deep neural network for chemical property prediction that learns
in a weak-supervised manner from large unlabeled chemical databases. When
coupled with transfer learning approaches to predict other smaller datasets for
chemical properties that it was not originally trained on, we show that
ChemNet's accuracy outperforms contemporary DNN models that were trained using
conventional supervised learning. Furthermore, we demonstrate that the ChemNet
pre-training approach is equally effective on both CNN (Chemception) and RNN
(SMILES2vec) models, indicating that this approach is network architecture
agnostic and is effective across multiple data modalities. Our results indicate
a pre-trained ChemNet that incorporates chemistry domain knowledge, enables the
development of generalizable neural networks for more accurate prediction of
novel chemical properties.