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

Autor: Munson BP; Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA.; Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA., Chen M; Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA., Bogosian A; Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA., Kreisberg JF; Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA., Licon K; Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA., Abagyan R; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, 92093, USA., Kuenzi BM; Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA., Ideker T; Division of Human Genomics and Precision Medicine, Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA. tideker@health.ucsd.edu.; Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA. tideker@health.ucsd.edu.; Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, 92093, USA. tideker@health.ucsd.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 May 06; Vol. 15 (1), pp. 3636. Date of Electronic Publication: 2024 May 06.
DOI: 10.1038/s41467-024-47120-y
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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