Application of Artificial Intelligence for the Prediction of Solvation Free Energies for Covid-19 Drug Discovery

Abstract

Solvation free energy is a key indicator of the effectiveness of a drug molecule. There are several applications of predicting the solvation free energies of chemical compounds using quantum mechanical methods. However, these methods take a long time and are costly. For that reason, the application of recently developed artificial intelligence techniques for the prediction of solvation free energies is becoming increasingly valuable in drug discovery to address time and the high-cost issues with traditional quantum mechanical approaches. In this paper, we present application of two different artificial intelligence models for predicting solute-solvent free solvation energy for Covid-19 drug design. The research involves building, training, evaluating and comparing the performances of the two models on a large dataset, then predicting solvation free energies for 138 known APIs and 28 organic solvents that could potentially be used as a Covid-19 medicine. The potential repurposing of 138 drugs for Covid-19 from solubility perspective is novel. We demonstrate the application of the AI models and derive several conclusions regarding suitability of the APIs and their efficacy. We conclude our research by providing insights on how our work can be put to future use towards drug development.