MolExplain shows which molecule parts affect drug property predictions
MolExplain: An Interactive Tool for Explainable Molecular Property Prediction
Human-Computer Interaction
Summary
Predicting how molecules behave is important for making new medicines, but often the predictions come without explanations. The authors created MolExplain, a tool that not only predicts a molecule's properties but also shows which parts of the molecule influence the prediction. This helps scientists understand why a molecule might work or not. For example, the tool correctly highlighted the chemical groups known to help certain drugs pass through cell membranes.
What this means in practice
- •For drug discovery teams: Use MolExplain to identify which structural features affect molecule permeability during drug design.
- •For chemical informatics developers: Integrate explainability methods like SHAP with molecular fingerprints to improve model transparency in chemistry software.
Authors
Pirm Dhararag, Dylan Cashman
Abstract
The application of machine learning to molecular property prediction has become increasingly prevalent in drug discovery, yet most models operate as black boxes, returning a prediction without revealing which structural features drive it. MolExplain addresses this gap by combining property prediction with sub-structure level visual explainability in an interactive web interface. The system featurizes molecules as Morgan fingerprints, classifies them using a trained XGBoost model, and applies SHAP attribution to produce a smooth heatmap overlay indicating which regions of the molecule contribute for or against the predicted property. Applied to cyclic peptide membrane permeability, the tool's attribution independently recovers the known role of backbone N-methylation in improving passive membrane diffusion, consistent with established chemistry. While demonstrated on cyclic peptides, the framework is designed to generalize to other molecular properties, positioning MolExplain as a platform for interactive, explainability-driven molecular design.