This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Researchers today announced AdaptiveFlow, an AI-informed platform that can virtually screen billions of drug-like molecules with a 1,000-fold reduction in computational costs over existing methods. Developed and validated by scientists from St.
Jude Children's Research Hospital, University of Pavia, Dana Farber Cancer Institute and Harvard Medical School, AdaptiveFlow allows prohibitively expensive ultra-large virtual drug screens to be conducted routinely. The open-source platform was published today in Nature Biotechnology. The platform's framework demonstrated linear scaling up to 5.6 million virtual central processing units (CPUs)—a new benchmark for cloud-based drug discovery—allowing billions of molecules to be screened without loss of efficiency.
As proof of concept, the team identified potent inhibitors for existing and emerging cancer targets for which few inhibitors are known. "AdaptiveFlow is the next generation in automated drug discovery platforms for routine ultra-large virtual screenings," said co-corresponding author Christoph Gorgulla, Ph.D., Center of Excellence for Data-Driven Discovery, St. Jude Department of Structural Biology.
"With this platform, we are able to screen 69 billion molecules, representing the largest ready-to-dock library in the world." The recent expansion of ultra-large molecule screening libraries prompted the drug discovery field to unlock their potential. While pioneering efforts saw success in billion-compound screens, AdaptiveFlow is the first of a new generation focused on efficiency, affordability and access. This is rooted in its economical approach to computation.
Gorgulla explained, "A lot of software loses communication efficiency with increasing CPU count, but with AdaptiveFlow, the scaling behavior is perfectly linear, even with millions of CPUs. That is special." At the core of AdaptiveFlow is an 18-dimensional grid in which each dimension represents a specific molecular property, such as molecular weight. This framework allows researchers to prioritize chemically diverse molecules and guides the rational selection of promising library subsets for deeper screening.
A machine-learning classification model trained on these prescreening results then identifies the most promising molecules. The molecules are then virtually screened against the protein target with over 1,500 supported docking protocols and subsequently ranked according to their predicted binding affinity. "The initial hits are often of considerably better quality than traditional methods, which can save researchers much time and effort during the optimization phases of drug discovery," Gorgulla said.
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