SYNAPX: Explainable Anticancer Drug Synergy Prediction via SHAP Analysis

Abstract

Identifying synergistic anti-cancer drug combinations is crucial for improving efficacy and reducing toxicity, but exhaustive experimental screening is prohibitively costly. We present SYNAPX, an explainable deep learning framework for drug synergy prediction that integrates chemical features of drug pairs with gene expression profiles of cancer cell lines. Our model combines ECFP6 fingerprints, physicochemical descriptors, toxicophore features, and transcriptomic features into a unified representation, and uses a fully connected neural network to predict continuous synergy scores. We further apply SHAP (SHapley Additive exPlanations) for biological interpretation to quantify feature contributions and explain individual predictions. Evaluated on the drug combination dataset of 23,052 oncology drug-combination samples, the method achieves strong predictive performance, including a ROC AUC of 0.9169, PR AUC of 0.7139, and balanced accuracy of 0.8289 after threshold optimization. Our explanation analysis shows that the most influential features are primarily molecular fingerprints and physicochemical descriptors, and a case study on the Methotrexate-BEZ-235 combination yields explanations consistent with known biological mechanisms.