南通大学学报（自然科学版）2025，Vol.24Issue(1)：10-17,8.DOI:10.12194/j.ntu.20240513001
基于图注意力网络的抗癌药物组合协同性预测方法
A prediction method for anti-cancer drug combinations synergy based on graph attention network
摘要
Abstract
Screening for synergistic anticancer drug combinations is essential for clinical treatment.However,the expo-nential 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 anti-cancer 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,MFGSyne-rgy 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 F1 scores 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,under-scoring MFGSynergy's superior generalization.关键词
抗癌药物联合治疗/分子指纹/图注意力神经网络/深度学习Key words
combination therapy with anti-cancer drugs/molecular fingerprint/graph attention neural network/deep learning分类
计算机与自动化引用本文复制引用
秦伟琦,包欣,陈晓,邱建龙,王东琳..基于图注意力网络的抗癌药物组合协同性预测方法[J].南通大学学报（自然科学版）,2025,24(1):10-17,8.基金项目
国家自然科学基金面上基金项目(62173175,61877033) （62173175,61877033）
