From Prediction to Insight: Visual Analytics for Understanding Compound Potency Models

Abstract

Machine learning (ML) is widely used in medicinal chemistry, but accurate predictions alone are insufficient. Researchers need insight into which molecular features determine compound properties. We present an application-oriented case study that analyzes a trained model for compound potency as a source of domain knowledge. The model is converted into decision rules, and topic-guided visual analytics is used to identify co-occurring feature conditions associated with high predicted potency. These patterns are then mapped back to molecular substructures, yielding chemically interpretable motifs and testable hypotheses about structure–activity relationships. The study demonstrates how combining rule-based representations, topic modeling, and visual exploration can turn potency predictions into mechanistic insight, and outlines a reusable workflow for interpreting ML models of molecular properties.