AI Could Accelerate Drug Discovery. But Only If We Can Trust It

Jiankun Lyu, PhD

One of the most consequential advances in artificial intelligence isn’t an eerily conversational chatbot—it’s a new way to unpack the unique 3D structures of proteins. This powerful deep-learning algorithm, dubbed AlphaFold, turns a process that once took scientists years to complete in the lab into a computer program that could run in less than an hour. The implications for medicine are immense: once the molecular nuances of a protein’s structure have been identified, researchers can begin to target it with drugs, correcting dysfunctions, combating infections, and improving health. But before AI can transform biomedicine, researchers will need to demonstrate that the algorithm’s predictions are as accurate as results obtained from tried-and-true experimental methods of the past, such as X-ray crystallography. A new paper in Science suggests this may now be the case. When researchers used sophisticated software to sift through billions of compounds—searching for potential new drugs by matching them against protein structures—they found that structures predicted by AlphaFold2 could, at least in some cases, effectively replace structures determined experimentally. The article, published May 16, 2024, is titled “AlphaFold2 Structures Guide Prospective Ligand Discovery.”

Login Or Register To Read Full Story