From Prediction to Insight: Visual Analytics for Understanding Compound Potency Models
IEEE Computer Graphics and Applications
|May 27, 2026
Summary
This study transforms machine learning (ML) potency predictions into chemical insights. By converting ML models into rules and using visual analytics, researchers can uncover molecular features driving compound properties for drug discovery.
Area of Science:
- Medicinal Chemistry
- Cheminformatics
- Computational Chemistry
Background:
- Machine learning (ML) is integral to medicinal chemistry for predicting compound properties.
- Accurate predictions are insufficient; understanding the underlying molecular features driving these properties is crucial for drug discovery.
- Current methods lack robust ways to translate complex ML models into actionable chemical knowledge.
Purpose of the Study:
- To develop and demonstrate a workflow for extracting domain knowledge from trained ML models in medicinal chemistry.
- To translate ML model predictions into chemically interpretable insights regarding compound potency.
- To generate testable hypotheses about structure-activity relationships (SAR).
Main Methods:
- An application-oriented case study analyzing a trained ML model for compound potency.
- Conversion of the ML model into a set of decision rules.
- Application of topic-guided visual analytics to identify patterns in feature conditions associated with high predicted potency.
- Mapping identified patterns back to molecular substructures to derive chemically relevant motifs.
Main Results:
- Successfully converted a predictive ML model into interpretable decision rules.
- Identified co-occurring molecular feature conditions linked to high compound potency using visual analytics.
- Generated chemically meaningful motifs and hypotheses regarding SAR from ML model insights.
- Demonstrated the utility of combining rule-based representations, topic modeling, and visual exploration.
Conclusions:
- The presented workflow effectively transforms ML potency predictions into mechanistic insights for medicinal chemistry.
- Combining rule-based models, topic modeling, and visual analytics provides a reusable approach for interpreting ML models of molecular properties.
- This method enhances the utility of ML in drug discovery by providing interpretable SAR and guiding experimental design.
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