Structure-Based Protein-Drug Affinity Prediction with Spatial Attention Mechanisms

Abstract

Discovery of new drugs heavily relies on predicting the binding affinities of drug molecules to suitable drug targets. To accelerate this process of identifying accurate affinities, computer-aided methods need to be applied in drug discovery pipeline. While various computational methods have been developed in the past ten years, the most successful methods to date use 3D convolutional neural networks (3D-CNNs). These 3D-CNN networks are based on deep learning models, and are both faster and more accurate than machine learning methods. However, currently used CNN is difficult to learn global and spatial features, while we hypothesis that spacial features should be critical for structure-based binding affinity predictions. Here we propose an end-to-end 3D-CNN with spatial attention mechanisms, called saCNN, to encourage spatial feature learning. When visualizing the learned spacial attentions in our experiments, it can be observed that saCNN model focuses more on the voxels near interaction centers. This key observation well supports our hypothesis that spacial features are critical for binding affinity predictions. In additions, we show that our model improves the Root Mean Square Error (RMSE) of the predicted binding affinities by 11.5% (with an absolute value of 1.117) and the Pearson Correlation Coefficient (R) by 3.2% (with an absolute value of 0.865) compared to currently used models on the PDBbind v.2016 core set. Importantly, the generalization abilities of our model is further demonstrated on CASF-2013 and CASF-2007 datasets.