Generative Recurrent Network For Design SARS-CoV-2 Main Protease Inhibitor

Abstract

Deep learning was adopted in de novo drug design for its generative ability in generating novel molecules, by training on a small set of molecules with known biological activity towards the target, the model will be finetuned to generate similar molecules. We proposed a method similar to the process found in evolution algorithms from creating, evaluating, and selecting from a population for fine-tuning the generative model without the need for molecules with known biological activity and applied it to the SARS-CoV-2, the proposed method decreases the time required to search for SARS-CoV-2 main protease inhibitors by developing a predictive model for predicting the affinity score of the molecules which decreases the time needed for docking to a fraction of the original time, we achieved 97.6 % accuracy in predicting the affinity score of molecules thus speeding up the search for existing molecules and the fine-tuning of the generative model to design protease inhibitors for SARS-CoV-2.