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

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 (<i>R</i>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. <i>JACS</i> <b>1979</b>). 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 <i>R</i>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.