Small molecules called immunomodulators can help create more effective vaccines and stronger immunotherapies to treat cancer.
But finding the molecules that instigate the right immune response is difficult —the number of drug-like small molecules has been estimated to be 10^60, much higher than the number of stars in the visible universe.
In a potential first for the field of vaccine design, machine learning guided the discovery of new immune pathway-enhancing molecules and found one particular small molecule that could outperform the best immunomodulators on the market. The results are published in the journal Chemical Science.
“We used artificial intelligence methods to guide a search of a huge chemical space,” said Prof. Aaron Esser-Kahn, co-author of the paper who led the experiments. ”In doing so, we found molecules with record-level performance that no human would have suggested we try. We’re excited to share the blueprint for this process.”
“Machine learning is used heavily in drug design, but it doesn’t appear to have been previously used in this manner for immunomodulator discovery,” said Prof. Andrew Ferguson, who led the machine learning. “It’s a nice example of transferring tools from one field to another.”
2023-11-18 19:41:03
Post from phys.org