Business
Recursion Bridges the Protein and Chemical Space with Massive Protein-Ligand Interaction Predictions Spanning 36 Billion Compounds
Recursion has predicted the protein target(s) for approximately 36 billion chemical compounds in the Enamine REAL Space, reported to be the world’s largest
About this update from Recursion Pharmaceuticals, Inc.
[{"type":"text","content":"Recursion has predicted the protein target(s) for approximately 36 billion chemical compounds in the Enamine REAL Space, reported to be the world’s largest searchable chemical library. These advances were made possible by NVIDIA DGX Cloud supercomputing and the recent acquisition of Cyclica’s MatchMaker technology.\nSALT LAKE CITY, Aug. 08, 2023 (GLOBE NEWSWIRE) -- Recursion (NASDAQ: RXRX), a leading clinical stage TechBio company decoding biology to industrialize drug discovery, today announced it has successfully screened the Enamine REAL Space chemical library using its MatchMaker technology, recently acquired from Cyclica, to predict the protein target(s) for approximately 36 billion chemical compounds. This accomplishment was made possible by several other enabling discoveries, including the predicted structures derived from the AlphaFold2 database for more than 15,000 human proteins containing more than 80,000 potential binding pockets, as well as the Enamine REAL Space, which is reported to be the world’s largest searchable chemical library comprised of approximately 36 billion make-on-demand molecules. In total, this screen digitally evaluated more than 2.8 quadrillion small molecule-target pairs. “This achievement represents a significant and exciting step toward achieving our mission of decoding biology and chemistry,” said Chris Gibson, Ph.D., Co-Founder and CEO of Recursion. “Until this point, the groundbreaking progress across biology and chemistry that enabled this moment – namely, AlphaFold, the Enamine virtual chemical library and the rapid advancement of large-scale compute and new machine learning approaches – have largely lived in isolation of one another or have been bridged at relatively small scales. Leveraging Recursion’s machine learning and computational expertise and NVIDIA’s technology, we have layered these advances together to predict how each of the molecules in this vast chemical universe may interact with the protein universe.” The company generated this massive new data layer of predicted interactions in less than 90 days after closing the acquisition of Cyclica and in under 30 days since initiating the collaboration with NVIDIA. MatchMaker uses machine learning to assess whether a small molecule is compatible with a specific protein binding pocket, providing a solution that is significantly less...