Interpretable Prediction of ProteinLigand Interaction by Convolutional Neural Network Evaluation of proteinligand interaction is a crucial step in the process of drug discovery. Recently, several methods based on deep learning have gained impressive binary classification performance on proteinligand binding prediction. However, lack of threedimensional complex data still limits the accuracy and robustness of evaluation of proteinligand binding affinity, as well as the prediction of their binding sites. In this paper, we propose a novel convolutional neural network based method for estimating the binding affinity between protein and ligand using only 1D sequence data. Even with the same amount of sample size, this model outperforms other structuredependent traditional and machine learning based methods in terms of both binary classification and regression task. Furthermore, we use this model to identify the key amino acid residues of protein that are vital for binding interaction, which provides biological interpretation.

DeepAtom: A Framework for ProteinLigand Binding Affinity Prediction The cornerstone of computational drug design is the calculation of binding affinity between two biological counterparts especially a chemical compound, i.e. a ligand, and a protein. Predicting the strength of proteinligand binding with reasonable accuracy is critical for drug discovery. In this paper, we propose a datadriven framework named DeepAtom to accurately predict the proteinligand binding affinity. With 3D Convolutional Neural Network (3DCNN) architecture, DeepAtom could automatically extract binding related atomic interaction patterns from the voxelized complex structure. Compared with the other CNN based approaches, our lightweight model design effectively improves the model representational capacity, even with the limited available training data. With validation experiments on the PDBbind v.2016 benchmark and the independent Astex Diverse Set, we demonstrate that the less feature engineering dependent DeepAtom approach consistently outperforms the other stateoftheart scoring methods. We also compile and propose a new benchmark dataset to further improve the model performances. With the new dataset as training input, DeepAtom achieves Pearsonu0027s  mathrmR0.83  and  textRMSE1.23  pK  units on the PDBbind v.2016 core set. The promising results demonstrate that DeepAtom models can be potentially adopted in computational drug development protocols such as molecular docking and virtual screening.