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\u2019s largest virtual drug screens.<\/p>\n
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. With libraries growing faster than the speed of the computers needed to process them, the screening of a modern billion-scale compound library against only a single drug target can take several months or years \u2013 even when using state-of-the-art supercomputers. Therefore, quite evidently, faster approaches are desperately needed. <\/p>\n
In a recent study published in the Journal of Chemical Information and Modeling, Dr Ina P\u00f6hner and colleagues from the University of Eastern Finland\u2019s School of Pharmacy teamed up with the host organisation of Finland\u2019s powerful supercomputers, CSC – IT Center for Science Ltd. – and industrial collaborators from Orion Pharma to study the prospect of machine learning in the speed-up of giga-scale virtual screens. <\/p>\n