F‑CPI: A Multimodal Deep Learning Approach for Predicting Compound Bioactivity Changes Induced by Fluorine Substitution

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 3CLpro by F-substitution achieved a more than 100-fold increase
in bioactivity (IC50: 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.