F-CPI: A Multimodal Deep Learning Approach for Predicting Compound Bioactivity Changes Induced by Fluorine Substitution
Qian Zhang, Wenhai Yin, Xinyao Chen, Aimin Zhou, Guixu Zhang, Zhi Qing Zhao, Zhiqiang Li, Yan Zhang, Samuel J. Bunu, Jingshan Shen, Weiliang Zhu, Xiangrui Jiang, Zhijian Xu · Journal of Medicinal Chemistry · 2024
Fluorine (F) substitution is a common method of drug discovery and development. However, there are no accurate approaches available for predicting the bioactivity changes after F-substitution, as the effect of substitution on the interactions between compounds and proteins (CPI) remains a mystery. In this study, we constructed a data set with 111,168 pairs of fluorine-substituted and nonfluorine-substituted compounds. We developed a multimodal deep learning model (F-CPI). In comparison with traditional machine learning and popular CPI task models, the accuracy, precision, and recall of F-CPI (∼90, ∼79, and ∼45%) were higher than those of GraphDTA (∼86, ∼58, and ∼40%). The application of the F-CPI for the structural optimization of hit compounds against SARS-CoV-2 3CL pro by F-substitution achieved a more than 100-fold increase in bioactivity (IC 50: 0.23 μM vs 28.19 μM). Therefore, the multimodal deep learning model F-CPI would be a veritable and effective tool in the context of drug discovery and design.