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 1060, 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."
Machine learning to screen molecules
Immunomodulators work by changing the signaling activity of innate immune pathways within the body. In particular, the NF-κB pathway plays a role in inflammation and immune activation, while the IRF pathway is essential in antiviral response.
Earlier this year, the PME team conducted a high-throughput screen that looked at 40,000 combinations of molecules to see if any affected these pathways. They then tested the top candidates, finding that when those molecules were added to adjuvants — ingredients that help boost the immune response in vaccines — the molecules increased antibody response and reduced inflammation.
To find more candidates, the team used these results combined with a library of nearly 140,000 commercially available small molecules to guide an iterative computational and experimental process.
Graduate student Yifeng (Oliver) Tang used a machine learning technique called active learning, which blends both exploration and exploitation to efficiently navigate the experimental screening through molecular space. This approach learns from the data previously collected and finds potential high-performing molecules to be tested experimentally while also pointing out areas that have been under-explored and may contain some valuable candidates.
