Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning

Rishikesh Magar; Prakarsh Yadav; Amir Barati Farimani

In this paper, we have collected a dataset comprised of antibody-antigen sequences of variety of viruses including HIV, Influenza, Dengue, SARS, Ebola, Hepatitis, etc. with their patient clinical/biochemical IC50 data. Using this dataset (we call it VirusNet), we trained and benchmarked different shallow and deep ML models and selected the best performing model. Based on SARS 2006 neutralizing antibody scaffold 26 , we created thousands of potential antibody candidates by mutation and screened them with our best performing ML model. Finally, molecular dynamics (MD) simulations were performed on the neutralizing candidates to check their structural stability. We predict 8 structures that were stable over the course of simulation and are potential neutralizing antibodies for COVID-19.