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.
into a binding region of the target and computes a"docking score" to express how well they fit. This way, docking scores were first determined for all 1.56 billion molecules.
"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," Kalliokoski explains.
The study represented the first rigorous comparison of a machine learning-boosted docking tool with a conventional docking baseline on the giga-scale."We found the machine learning-boosted tool to reliably and repeatedly reproduce the majority of the top-scoring compounds identified by conventional docking in a significantly shortened time frame," Pöhner says.
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