Anticancer drug synergy prediction based on CatBoost

In the stratified 5-fold cross-validation, our method obtained excellent results, where, the receiver operating characteristic area under the curve (ROC AUC) is 0.9217, precision-recall area under the curve (PR AUC) is 0.4651, mean squared error (MSE) is 0.1365, and Pearson correlation coefficient is 0.5335. The performance is significantly better than three other advanced models. Additionally, when using SHapley Additive exPlanations (SHAP) to interpret the biological significance of the prediction results, we found that drug features played more prominent roles than cell line features, and genes associated with cancer development, such as PTK2, CCND1, and GNA11, played an important part in drug synergy prediction. Combining the experimental results, the model proposed in this study has a good prediction effect and can be used as an alternative method for predicting anticancer drug combinations.