Predicting Drug Protein Interaction using Quasi-Visual Question Answering System
Identifying novel drug-protein interactions is crucial for drug discovery. For this purpose, many machine learning-based methods have been developed based on drug descriptors and one-dimensional (1D) protein sequences. However, protein sequence cannot accurately reflect the interactions in 3D space. On the other hand, a direct input of 3D structure is of low efficiency due to the sparse 3D matrix, and is also prevented by limited number of co-crystal structures available for training. In this work, we propose an end-to-end deep learning framework to predict the interactions by representing proteins with 2D distance map from monomer structures (Image), and drugs with molecular linear notation (String), following the Visual Question Answering mode. For an efficient training of the system, we introduced a dynamic attentive convolutional neural network to learn fixed-size representations from the variable-length distance maps and a self-attentional sequential model to automatically extract semantic features from the linear notations. Extensive experiments demonstrate that our model obtains competitive performance against state-of the-art baselines on the DUD-E, Human and BindingDB benchmark datasets. Further attention visualization provides biological interpretation to depict highlighted regions of both protein and drug molecules.
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