Good news! Defeating viral pathogens one trick at a time and fast!
"... And because their generative model was also a foundation model, pre-trained on massive amounts of raw data, it was versatile enough to create new inhibitors for multiple protein targets without extra training or any knowledge of their 3D structure.
In the end, the team hit on four potential Covid-19 antivirals in a fraction of the time it would have taken had they used conventional methods. ..."
From the 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."
Fig. 1. Overview of our inhibitor discovery workflow driven by CogMol, a sequence-guided deep generative foundation model.
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