Enhancing Drug Synergy Combination: Integrating Graph Transformers and BiLSTM for Accurate Drug Synergy Prediction

Combination therapy of drugs showed significant potential in treating complex diseases by overcoming drug resistance and improving therapeutic efficacy. However, due to the rapid increase in the number of available drugs, the cost and time required for experimentally screening synergistic drug combinations became increasingly burdensome. In this work, we proposed a novel drug synergy prediction model called GraphTranSynergy, which utilized graph transformer and BiLSTM to capture the molecular structure of drugs and gene expression features of cell lines. GraphTranSynergy extracted graphical features of drug pairs through the graph transformer module and integrated information from the BiLSTM module to extract useful features from gene expression profiles of cell lines. The final prediction of drug synergy was made through a fully connected neural network. Our model achieved AUC and PRAUC scores of 0.94, outperforming most existing models. Independent test results demonstrated that GraphTranSynergy exhibited superior generalization ability on the AstraZeneca dataset, particularly excelling in ACC and TPR metrics. Through a series of experiments and analyses, our model not only improved prediction accuracy but also demonstrated advantages in biological interpretability.