Visualization of Substructure Contributing to Metabolic Stability using Morgan Fingerprint

The cost and time required for drug discovery have increased, prompting the need for more efficient methods to predict compound properties using artificial intelligence (AI). In particular, the prediction of absorption, distribution, metabolism, and excretion (ADME) properties is crucial. Intrinsic metabolic clearance (CLint) is an essential property in ADME because it affects the side effects or the dosage schedule. In silico machine learning (ML) models for estimating CLint have been developed, but due to the inherent complexity of AI techniques, it is difficult to obtain ideas for a more effective structure. Therefore, we employed SHAP (SHapley Additive exPlanations), an explainable AI (XAI) method, to elucidate the contributions of molecular substructures to CLint predictions. We constructed a random forest model, classified into low and high clearance categories. SHAP values were calculated to visualize the importance of features, and significant substructures influencing CLint were identified. The model demonstrated a high recall for predicting low-CLint compounds; however, its overall accuracy for high-CLint classification was low. This highlights its potential for filtering out low-CLint compounds rather than accurately identifying those with high-CLint. Visualization of SHAP values provided insights into substructure modifications to improve CLint, thus providing valuable guidance for drug optimization. This approach highlights the effectiveness of integrating explainable AI methods to improve the interpretability of ML models in drug discovery.