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Induced polarization plays a pivotal role in ligand-protein binding by enhancing both the specificity and strength of molecular interactions. As a ligand approaches a protein, their respective electronic clouds redistribute in response to each other’s electrostatic fields—a phenomenon governed by induced polarization. The response of a molecule’s electron density to an external field is quantitatively described by its polarizability tensor. In this study, we calculated polarizability tensors for thousands of drug-like molecules from the CHEMBL database, focusing on compounds targeting the Thrombin, Estrogen Receptor alpha, and Phosphodiesterase 5A proteins using Density Functional Theory (DFT). We show that a machine learning model based on atomic hybridization accurately predict the polarizabilities eigenvalues, calculated with DFT. Then, we build a neural network and random forest models to predict the IC50’s based on the same features. The success of these models, despite utilizing a limited number of features, underscores the critical role of induced polarizabilities in determining binding energies.