MD-Syn: synergistic drug combination prediction based on a multidimensional feature fusion method and attention mechanisms.

Drug combination therapies have shown promising therapeutic efficacy in complex diseases and demonstrated the potential to reduce drug resistance. However, the vast number of possible drug combinations makes it difficult to screen them all in traditional experiments. Although computational models have been developed to address this challenge, existing methods often struggle to fully capture the complex biological interactions underlying drug synergy, limiting their predictive accuracy and generalization. In this study, we proposed MD-Syn, a computational framework based on a multidimensional feature fusion method and multi-head attention mechanisms. Given drug pair-cell line triplets, MD-Syn considers both one- and two-dimensional feature spaces simultaneously. It consists of a one-dimensional feature embedding module (1D-FEM), a two-dimensional feature embedding module (2D-FEM), and a deep neural network-based classifier for synergistic drug combination prediction. MD-Syn achieved an area under the receiver operating characteristic curve (AUROC) of 0.919 in five-fold cross-validation, outperforming the state-of-the-art methods. Furthermore, MD-Syn showed comparable results across four independent datasets. In addition, the multi-head attention mechanisms not only learn embeddings from different feature aspects but also focus on essential interactive feature elements, improving the interpretability of MD-Syn. In summary, MD-Syn is an interpretable framework to prioritize synergistic drug combination pairs using chemical and cancer cell line gene expression profiles. To facilitate broader community access to this model, we have developed a web portal (https://labyeh104-2.life.nthu.edu.tw/) that enables customized predictions of drug combination synergy effects based on user-specified compounds.