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Virtual drug screening that provides possible drug candidates facilitates early-stage drug discovery. It works by large scale predicting native-like protein-ligand complexes (PLC) from an abundance of docking decoys. Many affinity predicting models currently in use fail to provide reliable prediction because of a lack of non-binding data during model training, lost critical physical-chemical features, and difficulties in learning abstract information with limited neural layers. In this paper, we developed a deep learning model, DeepBindBC for classifying putative ligands as binding or non-binding. Our model incorporates information of non-binding interactions, making it more suitable for real applications. ResNet model architecture and more detailed atom type representation guarantee implicit features can be learned more accurately. DeepBindBC identified a novel human pancreatic -amylase binder validated by a …