About: Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning
Creator	Barati, Amir Magar, Rishikesh Yadav, Prakarsh
source	ArXiv
abstract	The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. Recent outbreak of novel coronavirus infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of COVID-19 will save the life of thousands. In this paper, we devised a machine learning (ML) model to predict the possible inhibitory synthetic antibodies for Corona virus. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, we screened thousands of hypothetical antibody sequences and found 8 stable antibodies that potentially inhibit COVID-19. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit the Corona virus.
has issue date	2020-03-18(xsd:dateTime)
has license	arxiv
sha1sum (hex)	cb57b7ff2b6e855e2cc748c93d2ba33259e66413
resource representing a document's title	Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning
resource representing a document's body	covid:cb57b7ff2b6e855e2cc748c93d2ba33259e66413#body_text
is schema:about of	named entity 'immune system' named entity 'epitopes' named entity 'COVID-19' named entity 'Corona' named entity 'inhibit' »more»

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