Anti-virus Autobots: Predicting More Infectious Virus Variants for Pandemic Prevention through Deep Learning

• URL: http://arxiv.org/abs/2205.14854v1
• Date: Mon, 30 May 2022 05:04:40 GMT
• Title: Anti-virus Autobots: Predicting More Infectious Virus Variants for Pandemic Prevention through Deep Learning
• Authors: Glenda Tan Hui En, Koay Tze Erhn, Shen Bingquan
• Abstract summary: More infectious virus variants can arise from rapid mutations in their proteins. These variants can evade one's immune system and infect vaccinated individuals, lowering vaccine efficacy. This project proposes Optimus PPIme - a deep learning approach to predict future, more infectious variants from an existing virus.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: More infectious virus variants can arise from rapid mutations in their proteins, creating new infection waves. These variants can evade one's immune system and infect vaccinated individuals, lowering vaccine efficacy. Hence, to improve vaccine design, this project proposes Optimus PPIme - a deep learning approach to predict future, more infectious variants from an existing virus (exemplified by SARS-CoV-2). The approach comprises an algorithm which acts as a "virus" attacking a host cell. To increase infectivity, the "virus" mutates to bind better to the host's receptor. 2 algorithms were attempted - greedy search and beam search. The strength of this variant-host binding was then assessed by a transformer network we developed, with a high accuracy of 90%. With both components, beam search eventually proposed more infectious variants. Therefore, this approach can potentially enable researchers to develop vaccines that provide protection against future infectious variants before they emerge, pre-empting outbreaks and saving lives.

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