Medicinal synergy prediction is a powerful tool in drug discovery and development that harnesses the principles of combination therapy to enhance therapeutic outcomes by improving efficacy, reducing toxicity, and preventing drug resistance. While a myriad of computational methods has emerged for predicting synergistic drug combinations, a large portion of them may overlook the intricate, yet critical relationships between various entities in drug interaction networks, such as drugs, cell lines, and diseases. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy. We introduce a salient deep hypergraph learning method, namely, Heterogeneous Entity Representation for MEdicinal Synergy prediction (HERMES), to predict anti-cancer drug synergy. HERMES integrates heterogeneous data sources, encompassing drug, cell line, and disease information, to provide a comprehensive understanding of the interactions involved. By leveraging advanced hypergraph neural networks with gated residual mechanisms, HERMES can effectively learn complex relationships/interactions within the data. Our results show HERMES demonstrates state-of-the-art performance, particularly in forecasting new drug combinations, significantly surpassing previous methods. This advancement underscores the potential of HERMES to facilitate more effective and precise drug combination predictions, thereby enhancing the development of novel therapeutic strategies.

翻译：药物协同作用预测是药物发现与开发中的一项有力工具，它利用联合治疗原理，通过提高疗效、降低毒性和防止耐药性来改善治疗结果。尽管已有众多计算方法用于预测协同药物组合，但其中大部分可能忽略了药物相互作用网络中各种实体（如药物、细胞系和疾病）之间复杂而关键的关系。这些关系具有复杂性和多维度性，需要精细建模以捕捉可能显著影响治疗效果的微妙相互作用。我们提出了一种显著的深度超图学习方法，即用于药物协同作用预测的异质实体表征（HERMES），以预测抗癌药物的协同作用。HERMES整合了异质数据源，包括药物、细胞系和疾病信息，以全面理解所涉及的相互作用。通过利用带有门控残差机制的先进超图神经网络，HERMES能够有效学习数据中的复杂关系/相互作用。我们的结果表明，HERMES展现了最先进的性能，特别是在预测新药物组合方面，显著超越了先前的方法。这一进展凸显了HERMES在促进更有效、更精确的药物组合预测方面的潜力，从而推动新型治疗策略的开发。