标题： MD-Syn：基于多维特征融合方法和注意力机制的协同药物组合预测 显示英文标题

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

摘要： 药物组合疗法在复杂疾病中显示出有希望的治疗效果，并表现出减少药物耐受性的潜力。 然而，可能的药物组合数量巨大，使得在传统实验中难以筛选所有组合。 在本研究中，我们提出了MD-Syn，这是一种计算框架，基于多维特征融合方法和多头注意力机制。 给定药物对-细胞系三元组，MD-Syn同时考虑一维和二维特征空间。 它由一个一维特征嵌入模块（1D-FEM）、一个二维特征嵌入模块（2D-FEM）以及基于深度神经网络的分类器组成，用于协同药物组合预测。 MD-Syn在五折交叉验证中达到了0.919的AUROC，优于最先进的方法。 此外，MD-Syn在两个独立数据集中表现出可比的结果。 此外，多头注意力机制不仅从不同的特征方面学习嵌入，还关注关键的交互特征元素，提高了MD-Syn的可解释性。 总之，MD-Syn是一个可解释的框架，用于优先选择具有化学物质和癌细胞系基因表达谱的协同药物组合对。 为了便于更广泛的社区访问此模型，我们开发了一个网络门户（https://labyeh104-2.life.nthu.edu.tw/），该门户可根据用户指定的化合物实现药物组合协同效应的定制预测。 显示英文摘要

摘要： 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.