Boosting virtual screening with machine learning allowed for a 10-fold time reduction in the processing of 1.56 billion drug-like molecules. Researchers from the University of Eastern Finland teamed up with industry and supercomputers to carry out one of the world's largest virtual drug screens.
In their efforts to find novel drug molecules, researchers often rely on fast computer-aided screening of large compound libraries to identify agents that can block a drug target. Such a target can, for instance, be an enzyme that enables a bacterium to withstand antibiotics or a virus to infect its host. The size of these collections of small organic molecules has seen a massive surge over the past years.
Before applying artificial intelligence to accelerate the screening, the researchers first established a baseline: In a virtual screening campaign of unprecedented size, 1.56 billion drug-like molecules were evaluated against two pharmacologically relevant targets over almost six months with the help of the supercomputers Mahti and Puhti, and molecular docking.
HASTEN uses machine learning to learn the properties of molecules and how those properties affect how well the compounds score. When presented with enough examples drawn from conventional docking, the machine learning model can predict docking scores for other compounds in the library much faster than the brute-force docking approach.
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