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

Abstract

Here, we use the frequency of the atomic hybridizations (s, sp, sp², and sp³) 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 (R²) 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 R² 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.