Rethinking cancer drug synergy prediction: a call for standardization in machine learning applications

Drug resistance poses a significant challenge to cancer treatment, often caused by intratumor heterogeneity. Combination therapies have been shown to be an effective strategy to prevent resistant cancer cells from escaping single-drug treatments. However, discovering new drug combinations through traditional molecular assays can be costly and time-consuming. In silico approaches can overcome this limitation by exploring many candidate combinations at scale. This study systematically evaluates the utility of various machine learning algorithms, input features, and drug synergy prediction tasks. Our findings indicate a pressing need for establishing a standardized framework to measure and develop algorithms capable of predicting synergy.