Fig. 1: Overview of the framework on intra- and inter-study drug combination predictions.

a The cross-validation strategy. We carry out the cross-validation in two steps: intra-study, which is five-fold cross-validation carried out within a single dataset, where the training and test sets are split by drug combination and cell lines, and inter-study, which is carried out between different datasets. The models used in the 1 vs. 1 inter-study cross-validations are the models generated from the inter-study training step. For the 3 vs. 1 inter-study cross-validation, three of the four datasets are combined and used as the training set to generate five models by five-fold cross-validation and then tested on the remaining dataset. b The overlapped information (drug, cell line, and treatment-cell line combination) between the four datasets used in this study. c The schematic of model construction in this study. We use four different data sources to generate the machine learning model used in this study. For drug-related features, we used chemical structure, monotherapy efficacy score, and their corresponding dose–response relationship. For the treated cancer cell lines, we used the transcription levels of 293 cancer-related genes. The constructed features are input into a lightGBM learner to generate models predicting the six different response metrics of the combination treatment: CSS, which is the sensitivity score representing the efficacy of the combination, and five synergy scores (S, Bliss, HSA, Loewe, and ZIP) representing the degree of interaction between the two drugs.