Explainable Active Learning Framework for Ligand Binding Affinity Prediction

Abstract

Active learning (AL) prioritises which compounds to measure next for protein–ligand affinity when assay or simulation budgets are limited. We present an explainable AL framework built on Gaussian process regression and assess how molecular representations, covariance kernels, and acquisition policies affect enrichment across four drug-relevant targets. Using recall of top active compound, we find that dataset identity which is target’s chemical landscape sets the performance ceiling and method choices modulate outcomes rather than overturn them. Fingerprints with simple Gaussian process kernels provide robust, low-variance enrichment, whereas learned embeddings with non-linear kernels can reach higher peaks but with greater variability. Uncertainty-guided acquisition consistently outperforms random selection, yet no single policy is universally optimal; the best choice follows structure-activity relationship (SAR) complexity. To move beyond black-box selection, we integrate SHapley Additive exPlanations (SHAP) to map high-impact fingerprint bits to chemically interpretable fragments over AL cycles, revealing how model focus sharpens onto SAR-relevant motifs. We release an interactive AL run analysis platform with SHAP traces to support reproducibility and target-specific decision making.