MIT’s machine learning designed a COVID-19 vaccine that could cover a lot more people

With that challenge in mind, scientists at Massachusetts Institute of Technology unveiled a machine learning approach that can predict the probability that a vaccine design will reach a certain proportion of the population.

“While they may protect more than 50% of the population, certain individuals and older individuals may not be protected,” Gifford told ZDNet.

It must be borne in mind that any vaccine design is only the beginning of a process that can take years to go through in vivo testing, in animals and then in humans, to establish both safety (non-toxicity), and efficacy, meaning that it confers a significant immune response.

Just identifying the relevant peptides, about 155,000 in this case, was the first challenge, breaking down the SARS-CoV-2 genetic sequence into its components.

Gifford and colleagues built a new program/ML algorithm namely OptiVax and Evalvax to assist the study.

OptiVax had to go to work on choosing amongst them to pick the best handful, or set, of peptides on which to focus.

A second program, called EvalVax, takes population data from thousands of individuals who self-reported across three categories, white, Black, and Asian.

All that translates into “about 12 hours on a large multiprocessor computer to design one vaccine using our methods,” said Gifford.

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