Problem
Research questions and friction points this paper is trying to address.
machine learning
molecular property prediction
explainability
drug discovery
MolExplain: An Interactive Tool for Explainable Molecular Property Prediction
🤖 AI Summary
MolExplain通过结合XGBoost模型预测与SHAP归因技术，提供分子性质预测的可视化解释，解决了机器学习模型在药物发现中作为黑盒的问题。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
interactive tool
explainable molecular property prediction
SHAP attribution
Morgan fingerprints
XGBoost model
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