Author:
Song Nan,Dong Ruihan,Pu Yuqian,Wang Ercheng,Xu Junhai,Guo Fei
Abstract
AbstractCompound–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.
Funder
National Natural Science Foundation of China
Natural Science Foundation of China of Zhejiang Province
National Key R&D Program of China
Excellent Young Scientists Fund in Hunan Province
Scientific Research Fund of Hunan Provincial Education Department
Zhejiang Lab Open Research Project
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Computer Graphics and Computer-Aided Design,Physical and Theoretical Chemistry,Computer Science Applications
Cited by
10 articles.
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