Explainability Methods from Machine Learning Detect Important Drugs’ Atoms in Drug-Target Interactions

Predicting drug-target interactions (DTI) with graph
neural networks
(GNNs) is hindered by their lack of interpretability. To address this,
we benchmark four explainable artificial intelligence (XAI) attribution
methods on GNN models trained for kinase and G-protein-coupled receptors
(GPCR) targets. We assess the methods’ consistency through
atom-level intersection over union (IoU) and validate their biological
relevance by mapping attributed atoms to three-dimensional (3D) protein–ligand
structures. While consistency across methods was modest, consensus
attributions were highly enriched for atoms directly contacting the
binding pocketup to 76% within 2 Å in the kinase-inhibitor
complexes. Notably, these attributed atoms were frequently found contacting
experimentally important regulatory residues such as those in the
DFG motif. This indicates that XAI methods, despite their disagreements,
can identify chemically meaningful ligand features, providing a foundation
for developing more interpretable GNNs in drug discovery.