Leveraging Machine Learning and Molecular Neural Networks to Interpret and Explain AI-Driven Prediction of Drug Efficacy

Drug discovery along with efficacy prediction through artificial intelligence (AI) requires researchers to build explainable models which offer interpretability. The introduced framework uses machine learning (ML) with molecular neural networks (MNNs) to reinforce both predictive accuracy and interpretability for drug efficacy models developed by AI. The proposed method utilizes molecular graph analysis together with graph neural networks (GNNs) and explainable AI (XAI) functions including SHAP (Shapley Additive Explanations) and Layer-wise Relevance Propagation (LRP) to explain model decision-making processes. The proposed model achieves superior accuracy and interpretability according to experimental results conducted on benchmark datasets comprising ChEMBL and DrugBank. The MNN implementation with explainability methods improves AI drug discovery dependability through showing which molecular attributes drive predictive outcomes. The study presents a solution that clarifies the relationship between AI prediction solutions and pharmaceutical expertise to build trust for pharmaceutical researchers.