Song Nan, Dong Ruihan, Pu Yuqian, Wang Ercheng, Xu Junhai, Guo Fei
School of New Media and Communication, Tianjin University, Tianjin, Tianjin, 300072, China.
College of Intelligence and Computing, Tianjin University, Tianjin, Tianjin, 300350, China.
J Cheminform. 2023 Oct 14;15(1):97. doi: 10.1186/s13321-023-00767-z.
Compound-protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate drug selectivity when binding to different targets. However, most selectivity prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model for compound-protein interaction prediction (PMF-CPI) and fine-tune it to assess drug selectivity. This model uses recurrent neural networks to process the protein embedding based on the pretrained language model TAPE, extracts molecular information from a graph encoder, and produces the output from dense layers. PMF-CPI obtained the best performance compared to outstanding approaches on both the binding affinity regression and CPI classification tasks. Meanwhile, we apply the model to analyzing drug selectivity after fine-tuning it on three datasets related to specific targets, including human cytochrome P450s. The study shows that PMF-CPI can accurately predict different drug affinities or opposite interactions toward similar targets, recognizing selective drugs for precise therapeutics.Kindly confirm if corresponding authors affiliations are identified correctly and amend if any.Yes, it is correct.
复合蛋白相互作用（CPI）在药物开发中起着重要作用。为避免副作用，评估药物与不同靶点结合时的选择性也至关重要。然而，大多数选择性预测模型是针对特定靶点构建的，数据有限。在本研究中，我们提出了一种用于复合蛋白相互作用预测的预训练多功能模型（PMF-CPI），并对其进行微调以评估药物选择性。该模型使用循环神经网络基于预训练语言模型TAPE处理蛋白质嵌入，从图编码器中提取分子信息，并通过密集层产生输出。在结合亲和力回归和CPI分类任务中，与优秀方法相比，PMF-CPI取得了最佳性能。同时，我们将该模型应用于在与特定靶点相关的三个数据集（包括人类细胞色素P450）上进行微调后分析药物选择性。研究表明，PMF-CPI可以准确预测不同药物对相似靶点的亲和力或相反相互作用，识别出用于精准治疗的选择性药物。
请确认相应作者的单位信息是否正确，如有需要请进行修改。
是的，信息正确。
