CENsible: Interpretable Insights into Small-Molecule Binding with Context Explanation Networks

We present a novel and interpretable approach for assessing
small-molecule
binding using context explanation networks. Given the specific structure
of a protein/ligand complex, our CENsible scoring function uses a
deep convolutional neural network to predict the contributions of
precalculated 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 precalculated term
to the final affinity prediction, with implications for subsequent
lead optimization.