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Instruments / Technical Area	Publication Details	Published	Actions
I04-1-Macromolecular Crystallography (fixed wavelength)	Accelerating drug target inhibitor discovery with a deep generative foundation model Vijil Chenthamarakshan , Samuel C. Hoffman , C. David Owen , Petra Lukacik , Claire Strain-Damerell , Daren Fearon , Tika R. Malla , Anthony Tumber , Christopher J. Schofield , Helen M. E. Duyvesteyn , Wanwisa Dejnirattisai , Loic Carrique , Thomas S. Walter , Gavin R. Screaton , Tetiana Matviiuk , Aleksandra Mojsilovic , Jason Crain , Martin A. Walsh , David I. Stuart , Payel Das Science Advances 9 DOI: 10.1126/sciadv.adg7865 Diamond Proposal Number(s): [27995] Open Access Show Abstract ▼ Abstract: Inhibitor discovery for emerging drug-target proteins is challenging, especially when target structure or active molecules are unknown. Here, we experimentally validate the broad utility of a deep generative framework trained at-scale on protein sequences, small molecules, and their mutual interactions—unbiased toward any specific target. We performed a protein sequence-conditioned sampling on the generative foundation model to design small-molecule inhibitors for two dissimilar targets: the spike protein receptor-binding domain (RBD) and the main protease from SARS-CoV-2. Despite using only the target sequence information during the model inference, micromolar-level inhibition was observed in vitro for two candidates out of four synthesized for each target. The most potent spike RBD inhibitor exhibited activity against several variants in live virus neutralization assays. These results establish that a single, broadly deployable generative foundation model for accelerated inhibitor discovery is effective and efficient, even in the absence of target structure or binder information.	Jun 2023

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I04-1-Macromolecular Crystallography (fixed wavelength)

Accelerating drug target inhibitor discovery with a deep generative foundation model