Volume 13, Number 4

Predicting more Infectious Virus Variants for Pandemic Prevention through Deep Learning

  Authors

Glenda Tan Hui En¹, KoayTze Erhn¹ and Shen Bingquan², ¹Raffles Institution, Singapore, ²DSO National Laboratories, Singapore

  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.

  Keywords

Virus Variants, Transformers, Deep Learning.