CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks.
bioRxiv
; 2023 Oct 21.
Article
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| MEDLINE
| ID: mdl-37904961
ABSTRACT
We present a novel and interpretable approach for predicting small-molecule binding affinities using context explanation networks (CENs). Given the specific structure of a protein/ligand complex, our CENsible scoring function uses a deep convolutional neural network to predict the contributions of pre-calculated terms to the overall binding affinity. We show that CENsible can effectively distinguish active vs. inactive compounds for many systems. Its primary benefit over related machine-learning scoring functions, however, is that it retains interpretability, allowing researchers to identify the contribution of each pre-calculated term to the final affinity prediction, with implications for subsequent lead optimization.
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2023
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