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

Qian Zhang¹, Wenhai Yin¹, Xinyao Chen^2,3

¹School of Computer Science and Technology, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, East China Normal University, Shanghai 200241, China.

Journal of Medicinal Chemistry

|December 20, 2024

Summary

This summary is machine-generated.

This study introduces F-CPI, a deep learning model to predict how fluorine substitution affects drug compound-protein interactions. F-CPI improves drug discovery by accurately forecasting bioactivity changes, outperforming existing methods.

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Area of Science:

Medicinal Chemistry
Computational Chemistry
Drug Discovery

Background:

Fluorine substitution is a key strategy in drug discovery.
Predicting bioactivity changes from fluorine substitution and its impact on compound-protein interactions (CPI) remains challenging.
Accurate prediction models for fluorine's effect on CPI are lacking.

Purpose of the Study:

To develop a predictive model for bioactivity changes induced by fluorine substitution.
To investigate the impact of fluorine substitution on compound-protein interactions (CPI).
To enhance the efficiency of drug discovery and design through accurate prediction.

Main Methods:

Construction of a large dataset comprising 111,168 pairs of fluorine-substituted and non-fluorine-substituted compounds.
Development of a multimodal deep learning model named F-CPI.
Comparative analysis of F-CPI against traditional machine learning and existing CPI models.

Main Results:

The F-CPI model demonstrated superior performance with accuracy (∼90%), precision (∼79%), and recall (∼45%) compared to GraphDTA.
F-CPI application led to over a 100-fold increase in bioactivity for SARS-CoV-2 3CLpro inhibitors.
Optimized compounds showed significantly improved inhibitory concentration 50 (IC50) values (0.23 μM vs 28.19 μM).

Conclusions:

The multimodal deep learning model F-CPI is an effective tool for predicting fluorine substitution effects on bioactivity.
F-CPI facilitates structural optimization in drug discovery, leading to significant bioactivity enhancements.
This model offers a valuable approach for advancing drug design and development processes.