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• W3005364306 abstract "Protein pocket matching, or binding site comparison, is of importance in drug discovery. Identification of similar binding pockets can help guide efforts for hit-finding, understanding polypharmacology, and characterization of protein function. The design of pocket matching methods has traditionally involved much intuition and has employed a broad variety of algorithms and representations of the input protein structures. We regard the high heterogeneity of past work and the recent availability of large-scale benchmarks as an indicator that a data-driven approach may provide a new perspective. We propose DeeplyTough, a convolutional neural network that encodes a three-dimensional representation of protein pockets into descriptor vectors that may be compared efficiently in an alignment-free manner by computing pairwise Euclidean distances. The network is trained with supervision (i) to provide similar pockets with similar descriptors, (ii) to separate the descriptors of dissimilar pockets by a minimum margin, and (iii) to achieve robustness to nuisance variations. We evaluate our method using three large-scale benchmark datasets, on which it demonstrates excellent performance for held-out data coming from the training distribution and competitive performance when the trained network is required to generalize to datasets constructed independently. DeeplyTough is available at https://github.com/BenevolentAI/DeeplyTough." @default.
• W3005364306 created "2020-02-14" @default.
• W3005364306 creator A5026733401 @default.
• W3005364306 creator A5064142146 @default.
• W3005364306 date "2020-02-05" @default.
• W3005364306 modified "2023-09-30" @default.
• W3005364306 title "DeeplyTough: Learning Structural Comparison of Protein Binding Sites" @default.
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• W3005364306 doi "https://doi.org/10.1021/acs.jcim.9b00554" @default.
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• W3005364306 hasPublicationYear "2020" @default.
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