Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides

Lujing Cao, Zhenyu Xu, Tianfeng Shang, Chengyun Zhang, Xinyi Wu, Yejian Wu, Silong Zhai, Zhajun Zhan, Hongliang Duan

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target “undruggable” proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeability, and researchers need much time and money to test this property in the laboratory. Herein, we propose an innovative multimodal model called Multi_CycGT, which combines a graph convolutional network (GCN) and a transformer to extract one- and two-dimensional features for predicting cyclic peptide permeability. The extensive benchmarking experiments show that our Multi_CycGT model can attain state-of-the-art performance, with an average accuracy of 0.8206 and an area under the curve of 0.8650, and demonstrates satisfactory generalization ability on several external data sets. To the best of our knowledge, it is the first deep learning-based attempt to predict the membrane permeability of cyclic peptides, which is beneficial in accelerating the design of cyclic peptide active drugs in medicinal chemistry and chemical biology applications.

原文English
頁(從 - 到)1888-1899
頁數12
期刊Journal of Medicinal Chemistry
67
發行號3
DOIs
出版狀態Published - 8 2月 2024

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