Scientists at Eli Lilly have been surprised by novel design of molecules that AI has produced as part of hypothetical drug discovery research.
The top AI-generated biological designs, molecules that Rau described as having"weird-looking structures" that could not be matched to much in the company's existing molecular database, but that looked like potentially strong drug candidates, were taken to Lilly research scientists. Executives, including Rau, expected scientists to dismiss the AI results.
According to executives working at the intersection of AI and health care, the field is on a trajectory that will see medicines completely generated by AI in the near future; according to some, within a few years at most it will become a norm in drug discovery.
It's a radical departure from the classic empirical method that has dominated the last century of drug discovery: extensive experimentation, subsequent gathering of data, analysis of the data on a human level, followed by another design process based on those results. Experimentation within the walls of a company followed by several decision points that scientists and executives hope will result in successful clinical trials."It's a very artisanal process," Powell said.
Daniel Diaz, a postdoctoral fellow in computer science who leads the deep proteins group at UT's Institute for Foundations of Machine Learning, said most current AI work on drugs is centered on small molecule discovery, but he thinks the bigger impact will be in the development of novel biologics , where he is already seeing how AI can speed up the process of finding the best designs.
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