论文标题
反病毒汽车人：通过深度学习预测大流行预防的更多传染性病毒变体
Anti-virus Autobots: Predicting More Infectious Virus Variants for Pandemic Prevention through Deep Learning
论文作者
论文摘要
更多的传染性病毒变体可能来自其蛋白质快速突变，从而产生新的感染波。这些变体可以逃避自己的免疫系统并感染接种疫苗的个体，从而降低疫苗功效。因此，为了改善疫苗设计，该项目提出了Optimus Ppime，这是一种深入学习的方法，可以预测现有病毒的未来，更具感染性的变体（以SARS-COV-2为例）。该方法包括一种算法，该算法充当攻击宿主细胞的“病毒”。为了增加感染性，“病毒”突变以更好地结合宿主的受体。尝试了2种算法 - 贪婪的搜索和光束搜索。然后，通过我们开发的变压器网络评估了这种变体主持结合的强度，高精度为90％。对于这两个组件，Beam搜索最终提出了更具感染性的变体。因此，这种方法可以潜在地使研究人员开发疫苗，以保护未来的感染性变种，然后再出现疫情并挽救生命。
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.
