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

The DOI matches its Crossref record; 49 of the 57 references it cites were found in a registry or an index.

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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Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Rappé, A. K., Casewit, C. J., Colwell, K. S., Goddard, W. A., & Skiff, W. M. (1992) · Journal of the American Chemical Society · doi:10.1021/ja00051a040 · Cited by 10,603
Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608
How to improve R&D productivity: the pharmaceutical industry's grand challenge
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., & Schacht, A. L. (2010) · Nature Reviews Drug Discovery · doi:10.1038/nrd3078 · Cited by 3,566
A Nonlinear Mapping for Data Structure Analysis
Sammon, J. W. (1969) · IEEE Transactions on Computers · doi:10.1109/t-c.1969.222678 · Cited by 3,364
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
Self-citation
Tensor-Train Decomposition
Oseledets, I. V. (2011) · SIAM Journal on Scientific Computing · doi:10.1137/090752286 · Cited by 2,860
ZINC: A Free Tool to Discover Chemistry for Biology
Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S. D., & Coleman, R. G. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci3001277 · Cited by 2,611

Show all 57 references

Research works printed but not linked: 2

Goodfellow, I. et al. Generative adversarial nets. in Advances in Neural Information Processing Systems 2672–2680 (2014).
Williams, R. J. Mach. Learn. 8, 229–256 (1992).

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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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