F-CPI: Prediction of activity changes induced by fluorine substitution using multimodal deep learning

Abstract

There are a large number of fluorine (F)-containing compounds in approved drugs, and F substitution is a common method in drug discovery and development. However, F is difficult to form traditional hydrogen bonds and typical halogen bonds. As a result, accurate prediction of the activity after F substitution is still impossible using traditional drug design methods, whereas artificial intelligence driven activity prediction might offer a solution. Although more and more machine learning and deep learning models are being applied, there is currently no model specifically designed to study the effect of F on bioactivities. In this study, we developed a specialized deep learning model, F-CPI, to predict the effect of introducing F on drug activity, and tested its performance on a carefully constructed dataset. Comparison with traditional machine learning models and popular CPI task models demonstrated the superiority and necessity of F-CPI, achieving an accuracy of approximately 89% and a precision of approximately 67%. In the end, we utilized F-CPI for the structural optimization of hit compounds against SARS-CoV-2 3CL^pro. Impressively, in one case, the introduction of only one F atom resulted in a more than 100-fold increase in activity (IC₅₀: 22.99 nM vs. 28190 nM). Therefore, we believe that F-CPI is a helpful and effective tool in the context of drug discovery and design.