MD-Syn: Synergistic drug combination prediction based on the multidimensional feature fusion method and attention mechanisms

TL;DR

MD-Syn is a novel computational framework that combines multidimensional feature fusion and attention mechanisms to accurately predict synergistic drug combinations, enhancing interpretability and providing a user-friendly web portal.

Contribution

The paper introduces MD-Syn, integrating multi-dimensional feature fusion with multi-head attention for improved drug synergy prediction and interpretability.

Findings

Achieved AUROC of 0.919 in cross-validation

Outperformed state-of-the-art methods

Demonstrated robustness on independent datasets

Abstract

Drug combination therapies have shown promising therapeutic efficacy in complex diseases and have demonstrated the potential to reduce drug resistance. However, the huge number of possible drug combinations makes it difficult to screen them all in traditional experiments. In this study, we proposed MD-Syn, a computational framework, which is based on the multidimensional feature fusion method and multi-head attention mechanisms. Given drug pair-cell line triplets, MD-Syn considers one-dimensional 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 the AUROC of 0.919 in 5-fold cross-validation, outperforming the state-of-the-art methods. Further, MD-Syn showed comparable results over two 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 with chemicals 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.