Work

Leveraging-Induced Polarization for Drug Discovery: Efficient IC50 Prediction Using Minimal Features

Year: 2025

Type: article

Abstract: Here, we use the frequency of the atomic hybridizations (s, sp, sp 2, and sp 3 ) of each atom type (H, C, N, O, S, etc.) within a molecule to predict the IC50s of drug-like molecules, focusing on compounds targeting the Thrombin, Estrogen Receptor alpha, and Phosphodiesterase 5A proteins. The Neural Network and Random Forest models yield high correlation coefficients ( R 2 ) and low mean square error (MSE) using only 19 features. The atomic hybridizations have been used previously to calculate the molecular polarizability using a simple empirical model (Miller et al. JACS 1979 ). We show that the atomic hybridizations may also be used to accurately predict the molecular polarizabilities of these molecules. The results show the importance of the induced polarization in protein–ligand binding. Furthermore, the variation in R 2 and MSE for the different target proteins indicates that the contribution of the induced polarization to the binding energies is different for different target proteins.

Source: Journal of Chemical Information and Modeling

DOI: 10.1021/acs.jcim.5c00076

Authors: Ashraf Mohamed, Bernard R. Brooks, Muhamed Amin

Institutions: The University of Texas at Austin, National Institutes of Health, National Heart, Lung, and Blood Institute, University of Groningen

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4408665066

FWCI: 1.441

Cites: 36

Cited by: 3

Related to: 10