A prediction method for anti-cancer drug combinations synergy based on graph attention network

Screening for synergistic anticancer drug combinations is essential for clinical treatment. However, the exponential rise in potential combinations renders traditional methods time-intensive and expensive, impeding the discovery of novel synergies. To overcome this, multi-scale feature fusion model based on graph attention network for anticancer synergistic drug combination prediction(MFGSynergy) is introduced, a graph attention network-based model to streamline anticancer drug combination screening. Initially, the model converts drug simplified molecular input line entry system(SMILES) into molecular graphs and fingerprint data while preprocessing cancer cell line data. It then employs a graph attention network(GAT) and multilayer perceptron(MLP) to extract features from both drug and cell line data, fusing these multi-source features to predict combination synergy. Evaluated on a public dataset, MFGSynergy outperforms Deep DDS, DeepSynergy, and six machine learning methods, achieving receiver operating characteri-stic area under the curve(ROC AUC), area under the precision-recall curve(PR AUC), accuracy(ACC), precision(PREC), true positive rate(TPR), and F1scores of 0.94, 0.94, 0.86, 0.87, 0.86, and 0.86, respectively, in five-fold cross-validation. Moreover, independent tests on unknown combinations validate its robust predictive power, underscoring MFGSynergy′s superior generalization.