  The discovery of novel inhibitor molecules for emerging drug-target proteins
is widely acknowledged as a challenging inverse design problem: Exhaustive
exploration of the vast chemical search space is impractical, especially when
the target structure or active molecules are unknown. Here we validate
experimentally the broad utility of a deep generative framework trained
at-scale on protein sequences, small molecules, and their mutual interactions
-- that is unbiased toward any specific target. As demonstrators, we consider
two dissimilar and relevant SARS-CoV-2 targets: the main protease and the spike
protein (receptor binding domain, RBD). To perform target-aware design of novel
inhibitor molecules, a protein sequence-conditioned sampling on the generative
foundation model is performed. Despite using only the target sequence
information, and without performing any target-specific adaptation of the
generative model, micromolar-level inhibition was observed in in vitro
experiments for two candidates out of only four synthesized for each target.
The most potent spike RBD inhibitor also exhibited activity against several
variants in live virus neutralization assays. These results therefore establish
that a single, broadly deployable generative foundation model for accelerated
hit discovery is effective and efficient, even in the most general case where
neither target structure nor binder information is available.
