DESIGNING POTENTIAL DRUGS THAT CAN TARGET SARS-COV2’S MAIN PROTEASE : A PROACTIVE DEEP TRANSFER LEARNING APPROACH USING LSTM ARCHITECTURE

Abstract

Drug discovery is a crucial step in the process of delivering a new drug to the market that can take up to 2-3 years which can be more penalizing given the current global pandemic caused by the outbreak of the novel coronavirus SARS-CoV 2. Artificial Intelligence methodologies have shown great potential in resolving tasks in various domains such as image classification, sound recognition, also in the range of the previous years, Artificial Intelligence proved to be the go-to for generative tasks for use cases such as music sequences, text generation and solving also problems in biology. The goal of this work is to harvest the power of these architectures using generative recurrent neural network with long short-term memory (LSTM) gating techniques in order to generate new and non-existing molecules that can bind to the main COVID-19 protease, which is a key agent in the transcription and replication of the virus, and thus can act as a potential drug that can neutralize the virus inside of an infected host. As of today, there are no specific targeted therapeutic agents to treat the disease and all existing treatments are all very limited. Known drugs that are passing clinical trials such as Hydroxychloroquine and Remdesivir showed respectively a binding energy with SARS-CoV-2’s main protease of -5.3 and -6.5, the results of the newly generated molecules exhibited scores ranging till -13.2. Covid-19, Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), SMILES, Generative recurrent neural networks, Protein-ligand docking, molecule generation, In-silico drug discovery