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 protein sequences. However, protein sequences cannot accurately reflect the interactions in three-dimensional space. However, direct input of three-dimensional structure is of low efficiency due to the sparse three-dimensional matrix, and is also prevented by the limited number of co-crystal structures available for training. Here we propose an end-to-end deep learning framework to predict the interactions by representing proteins with a two-dimensional distance map from monomer structures (Image) and drugs with molecular linear notation (String), following the visual question answering mode. For efficient training of the system, we introduce 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 directory of useful decoys, enhanced (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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All data used in this paper are publicly available and can be accessed at http://dude.docking.org for the DUD-E dataset, https://github.com/IBMInterpretableDTIP for the BindingDB-IBM dataset, https://github.com/masashitsubaki/CPI_prediction/tree/master/dataset for human dataset and https://www.rcsb.org for the protein crystal structure.
Demo, instructions and code for DrugVQA are available at https://github.com/prokia/drugVQA.
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The work was supported in part by the National Key R&D Program of China (2018YFC0910500), GD Frontier and Key Tech Innovation Program (2018B010109006,2019B020228001), the National Natural Science Foundation of China (61772566, U1611261 and 81801132, 81903540) and the programme for Guangdong Introducing Innovative and Entrepreneurial Teams (2016ZT06D211).
The authors declare no competing interests
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Zheng, S., Li, Y., Chen, S. et al. Predicting drug–protein interaction using quasi-visual question answering system. Nat Mach Intell 2, 134–140 (2020). https://doi.org/10.1038/s42256-020-0152-y