Xu Mengdie, Zhao Xinwei, Wang Jingyu, Feng Wei, Wen Naifeng, Wang Chunyu, Wang Junjie, Liu Yun, Zhao Lingling
Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China.
Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China.
J Cheminform. 2023 Mar 16;15(1):33. doi: 10.1186/s13321-023-00690-3.
Drug combination therapies are promising clinical treatments for curing patients. However, efficiently identifying valid drug combinations remains challenging because the number of available drugs has increased rapidly. In this study, we proposed a deep learning model called the Dual Feature Fusion Network for Drug-Drug Synergy prediction (DFFNDDS) that utilizes a fine-tuned pretrained language model and dual feature fusion mechanism to predict synergistic drug combinations. The dual feature fusion mechanism fuses the drug features and cell line features at the bit-wise level and the vector-wise level. We demonstrated that DFFNDDS outperforms competitive methods and can serve as a reliable tool for identifying synergistic drug combinations.
药物联合疗法是治疗患者的有前景的临床治疗方法。然而，由于可用药物的数量迅速增加，有效地识别有效的药物组合仍然具有挑战性。在本研究中，我们提出了一种名为用于药物-药物协同作用预测的双特征融合网络（DFFNDDS）的深度学习模型，该模型利用微调的预训练语言模型和双特征融合机制来预测协同药物组合。双特征融合机制在逐位级别和向量级别融合药物特征和细胞系特征。我们证明了DFFNDDS优于竞争方法，可以作为识别协同药物组合的可靠工具。
