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Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts
[article]
2019
arXiv
pre-print
Predicting compound-protein affinity is critical for accelerating drug discovery. Recent progress made by machine learning focuses on accuracy but leaves much to be desired for interpretability. Through molecular contacts underlying affinities, our large-scale interpretability assessment finds commonly-used attention mechanisms inadequate. We thus formulate a hierarchical multi-objective learning problem whose predicted contacts form the basis for predicted affinities. We further design a
arXiv:1912.12553v1
fatcat:sslcp6vt5nejnortd47aqzkopi