Model Interpretation and Explainability in Chemical Property Prediction

Cheminformatics › Machine Learning QSAR/QSPR Research

Model Interpretation and Explainability in Chemical Property Prediction

Interns will apply interpretability techniques (SHAP values, LIME, permutation importance) to understand how molecular features influence QSAR model predictions. They will generate chemical insights from model outputs and develop visualization methods to identify structure-activity relationships that are meaningful to medicinal chemists.

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