De novo generation of multi-target compounds using deep generative chemistry

Brenton P. Munson, Michael Chen, Audrey Bogosian, Jason F. Kreisberg, Katherine Licon, Ruben Abagyan, Brent M. Kuenzi and Trey Ideker ()
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Brenton P. Munson: University of California San Diego
Michael Chen: University of California San Diego
Audrey Bogosian: University of California San Diego
Jason F. Kreisberg: University of California San Diego
Katherine Licon: University of California San Diego
Ruben Abagyan: University of California San Diego
Brent M. Kuenzi: University of California San Diego
Trey Ideker: University of California San Diego

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Polypharmacology drugs—compounds that inhibit multiple proteins—have many applications but are difficult to design. To address this challenge we have developed POLYGON, an approach to polypharmacology based on generative reinforcement learning. POLYGON embeds chemical space and iteratively samples it to generate new molecular structures; these are rewarded by the predicted ability to inhibit each of two protein targets and by drug-likeness and ease-of-synthesis. In binding data for >100,000 compounds, POLYGON correctly recognizes polypharmacology interactions with 82.5% accuracy. We subsequently generate de-novo compounds targeting ten pairs of proteins with documented co-dependency. Docking analysis indicates that top structures bind their two targets with low free energies and similar 3D orientations to canonical single-protein inhibitors. We synthesize 32 compounds targeting MEK1 and mTOR, with most yielding >50% reduction in each protein activity and in cell viability when dosed at 1–10 μM. These results support the potential of generative modeling for polypharmacology.

Date: 2024
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DOI: 10.1038/s41467-024-47120-y

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