Cited by 1,573Computational Drug Discovery MethodsMachine Learning in Materials ScienceProtein Degradation and Inhibitors

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Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Zhavoronkov, Alex, Ivanenkov, Yan A., Aliper, Alex, Veselov, Mark S., Aladinskiy, Vladimir A., Aladinskaya, Anastasiya V., Terentiev, Victor A., Polykovskiy, Daniil A., Kuznetsov, Maksim D., Asadulaev, Arip, Volkov, Yury, Zholus, Artem, Shayakhmetov, Rim R., Zhebrak, Alexander, Minaeva, Lidiya I., Zagribelnyy, Bogdan A., Lee, Lennart H., Soll, Richard, Madge, David, Xing, Li, Guo, Tao, Aspuru-Guzik, Alán·2019·Nature Biotechnology

DOI 10.1038/s41587-019-0224-x

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Abstract

We have developed a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL optimizes synthetic feasibility, novelty, and biological activity. We used GENTRL to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days. Four compounds were active in biochemical assays, and two were validated in cell-based assays. One lead candidate was tested and demonstrated favorable pharmacokinetics in mice.

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Authors: Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., Volkov, Y., Zholus, A., Shayakhmetov, R. R., Zhebrak, A., Minaeva, L. I., Zagribelnyy, B. A., Lee, L. H., Soll, R., Madge, D., Xing, L., Guo, T., & Aspuru-Guzik, A.
Affiliations: WuXi AppTec (China) (CN)
Corresponding author: Zhavoronkov, Alex
ORCID: Zhavoronkov, Alex
Ivanenkov, Yan A.
Aliper, Alex
Veselov, Mark S.
Aladinskiy, Vladimir A.
Terentiev, Victor A.
Polykovskiy, Daniil A.
Kuznetsov, Maksim D.
Asadulaev, Arip
Zholus, Artem
Shayakhmetov, Rim R.
Zhebrak, Alexander
Minaeva, Lidiya I.
Zagribelnyy, Bogdan A.
Lee, Lennart H.
Guo, Tao
Venue: Nature Biotechnology
Publisher: Springer Science and Business Media LLC
Published: 2019-09
Volume: 37
Issue: 9
Pages: 1038-1040
ISSN: 0733-222X (ISSN-L)
1087-0156
1546-1696
DOI: 10.1038/s41587-019-0224-x
OpenAlex: W2971690404
Cited by: 1,573
References: 57
Citations by year: 2026: 320 · 2025: 306 · 2024: 224 · 2023: 178 · 2022: 169 · 2021: 206 · 2020: 144 · 2019: 22
FWCI: 92.35
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Nature Biotechnology (publishedVersion)
PubMed (publishedVersion)
Funders: Nvidia
Nature
School of Medicine, University of New Mexico
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords: DDR1, deep generative models, drug discovery, fibrosis, pharmacokinetics
MeSH terms: Discoidin Domain Receptor 1, Deep Learning, Animals, Dogs, Drug Evaluation, Preclinical, Enzyme Inhibitors, Humans, Microsomes, Liver, Models, Molecular, Protein Conformation, Molecular Structure, Mice, Rats

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Deep learning enables rapid identification of potent DDR1 kinase inhibitors

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