De Novo Design of Bioactive Small Molecules by Artificial Intelligence
Open accessAbstract
Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low-micromolar receptor modulatory activity in cell-based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry.
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Merk et al. trained a recurrent neural network to capture bioactive compounds such as SMILES strings; identified compounds were synthesized and demonstrated potent receptor modulatory activity in in vitro assays.
“Merk et al. trained a recurrent neural network to capture a large number of bioactive compounds such as SMILES strings. This model was then fine-tuned to recognize retinoid X and peroxisome proliferator-activated receptor agonists. The identified compounds were synthesized and demonstrated potent receptor modulatory activity in in vitro assays [23].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [23]
DNNs provide the possibility of inverting the QSAR model (inverse QSAR).
“They provide the possibility of inverting the QSAR model (inverse QSAR), i.e. designing molecules directly from the model (so called generative models).73”
From QSAR without borders (2020) · cites this paper as [73]
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
PubChem Substance and Compound databases
Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436 · see citing works
The PPARs: From Orphan Receptors to Drug Discovery
Willson, T. M., Brown, P. J., Sternbach, D. D., & Henke, B. R. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm990554g · Cited by 1,816 · see citing works
The ChEMBL bioactivity database: an update
Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J. K., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1031 · Cited by 1,487 · see citing works
Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0235-x · Cited by 1,358 · see citing works
International Union of Pharmacology. LXI. Peroxisome Proliferator-Activated Receptors
Michalik, L., Auwerx, J. H., Berger, J. P., Chatterjee, K., Glass, C. K., Gonzalez, F. J., Grimaldi, P. A., Kadowaki, T., Lazar, M. A., O’Rahilly, S., et al. (2006) · Pharmacological Reviews · doi:10.1124/pr.58.4.5 · Cited by 997 · see citing works
Computer-based de novo design of drug-like molecules
Schneider, G., & Fechner, U. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1799 · Cited by 956 · see citing works
Self-citationInternational Union of Pharmacology. LXIII. Retinoid X receptors
Germain, P., Chambon, P., Eichele, G., Evans, R. M., Lazar, M. A., Leid, M., De Lera, A. R., Lotan, R., Mangelsdorf, D. J., & Gronemeyer, H. (2006) · Pharmacological Reviews · doi:10.1124/pr.58.4.7 · Cited by 496 · see citing works
Generative Recurrent Networks for De Novo Drug Design
Gupta, A., Müller, A. T., Huisman, B. J. H., Fuchs, J. A., Schneider, P., & Schneider, G. (2017) · Molecular Informatics · doi:10.1002/minf.201700111 · Cited by 474 · see citing works
Self-citationApplication of Generative Autoencoder in <i>De Novo</i> Molecular Design
Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J., & Chen, H. (2018) · Molecular Informatics · doi:10.1002/minf.201700123 · Cited by 411 · see citing works
Synthesis and Structure-Activity Relationships of Novel Retinoid X Receptor-Selective Retinoids
Boehm, M. F., Zhang, L., Badea, B. A., White, S. K., Mais, D. E., Berger, E. M., Suto, C. M., Goldman, M. E., & Heyman, R. A. (1994) · Journal of Medicinal Chemistry · doi:10.1021/jm00044a014 · Cited by 373 · see citing works
SureChEMBL: a large-scale, chemically annotated patent document database
Papadatos, G., Davies, M., Dedman, N., Chambers, J. K., Gaulton, A., Siddle, J., Koks, R., Irvine, S. A., Pettersson, J., Goncharoff, N., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1253 · Cited by 233 · see citing works
Reker, D., Rodrigues, T., Schneider, P., & Schneider, G. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320001111 · Cited by 226 · see citing works
Self-citationIdentification of a subtype selective human PPARα agonist through parallel-array synthesis
Brown, P. J., Stuart, L. W., Hurley, K. P., Lewis, M. C., Winegar, D. A., Wilson, J. G., Wilkison, W. O., Ittoop, O. R. R., & Willson, T. M. (2001) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/s0960-894x(01)00188-3 · Cited by 178 · see citing works
De Novo Design at the Edge of Chaos
Schneider, P., & Schneider, G. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01849 · Cited by 167 · see citing works
Self-citationHartenfeller, M., & Schneider, G. (2010) · Methods in molecular biology · doi:10.1007/978-1-60761-839-3_12 · Cited by 160 · see citing works
Self-citationOpportunities and Challenges for Fatty Acid Mimetics in Drug Discovery
Proschak, E., Heitel, P., Kalinowsky, L., & Merk, D. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.6b01287 · Cited by 89 · see citing works
Self-citationSchmidt, J., Rotter, M., Weiser, T., Wittmann, S. K., Weizel, L., Kaiser, A., Heering, J., Goebel, T., Angioni, C., Wurglics, M., et al. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00398 · Cited by 87 · see citing works
Self-citationFinding Chemical Structures Corresponding to a Set of Coordinates in Chemical Descriptor Space
Miyao, T., & Funatsu, K. (2017) · Molecular Informatics · doi:10.1002/minf.201700030 · Cited by 11 · see citing works
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Research works printed but not linked: 2
- [5]A. Gupta, A. T. Müller, B. J. H. Huisman, J. A. Fuchs, P. Schneider, G. Schneider, Mol. Inf. 2018, 37, 1700111.
- [14]Reaxys, Elsevier 2017; https://www.reaxys.com/.
Cited by
Sorted by citations
Artificial intelligence in drug discovery and development
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695 · see citing works
The rise of artificial intelligence in healthcare applications
Bohr, A., & Memarzadeh, K. (2020) · Artificial Intelligence in Healthcare · doi:10.1016/b978-0-12-818438-7.00002-2 · Cited by 1,636 · see citing works
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
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., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573 · see citing works
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473 · see citing works
Drug discovery with explainable artificial intelligence
Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063 · see citing works
Self-citationArtificial intelligence in drug development: Present status and future prospects
Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018 · see citing works
Rethinking drug design in the artificial intelligence era
Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894 · see citing works
Self-citationMuratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., et al. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.565644 · Cited by 849 · see citing works
GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895 · see citing works
Deep learning for molecular design—a review of the state of the art
Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683 · see citing works
REINVENT 2.0: An AI Tool for De Novo Drug Design
Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00915 · Cited by 496 · see citing works
Polypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds
Proschak, E., Stark, H., & Merk, D. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b00760 · Cited by 498 · see citing works
Self-citationDeep learning in drug discovery: opportunities, challenges and future prospects
Lavecchia, A. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.07.006 · Cited by 353 · see citing works
Assessing the impact of generative AI on medicinal chemistry
Walters, W. P., & Murcko, M. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0418-2 · Cited by 262 · see citing works
Generative molecular design in low data regimes
Moret, M., Friedrich, L., Grisoni, F., Merk, D., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0160-y · Cited by 219 · see citing works
Self-citationChemistry42: An AI-Driven Platform for Molecular Design and Optimization
Ivanenkov, Y. A., Polykovskiy, D., Bezrukov, D., Zagribelnyy, B., Aladinskiy, V., Kamya, P., Aliper, A., Ren, F., & Zhavoronkov, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01191 · Cited by 231 · see citing works
A critical overview of computational approaches employed for COVID-19 drug discovery
Muratov, E. N., Amaro, R., Andrade, C. H., Brown, N., Ekins, S., Fourches, D., Isayev, O., Kozakov, D., Medina-Franco, J. L., Merz, K. M., et al. (2021) · Chemical Society Reviews · doi:10.1039/d0cs01065k · Cited by 206 · see citing works
Self-citationCombining generative artificial intelligence and on-chip synthesis for de novo drug design
Grisoni, F., Huisman, B. J. H., Button, A. L., Moret, M., Atz, K., Merk, D., & Schneider, G. (2021) · Science Advances · doi:10.1126/sciadv.abg3338 · Cited by 190 · see citing works
Self-citationThe Advent of Generative Chemistry
Vanhaelen, Q., Lin, Y.-C., & Zhavoronkov, A. (2020) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.0c00088 · Cited by 151 · see citing works
Autonomous Molecular Design: Then and Now
Dimitrov, T., Kreisbeck, C., Becker, J. S., Aspuru-Guzik, A., & Saikin, S. K. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b01226 · Cited by 103 · see citing works
RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design
Wang, M., Hsieh, C.-Y., Wang, J., Wang, D., Weng, G., Shen, C., Yao, X., Bing, Z., Li, H., Cao, D., et al. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00732 · Cited by 99 · see citing works
Coloring Molecules with Explainable Artificial Intelligence for Preclinical Relevance Assessment
Jiménez-Luna, J., Skalic, M., Weskamp, N., & Schneider, G. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01344 · Cited by 96 · see citing works
Self-citationCan Generative-Model-Based Drug Design Become a New Normal in Drug Discovery?
Chen, H. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c02042 · Cited by 21 · see citing works
- Authors
- Merk, D., Friedrich, L., Grisoni, F., & Schneider, G.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Schneider, Gisbert
- Venue
- Molecular Informatics
- Publisher
- Wiley
- Published
- 2018-01
- Volume
- 37
- Issue
- 1-2
- ISSN
- 1868-1743 (ISSN-L)
- 1868-1751
- PMC
- PMC5838524
- OpenAlex
- W2784270883
- Cited by
- 373
- References
- 19
- Citations by year
- 2026: 28 · 2025: 39 · 2024: 51 · 2023: 57 · 2022: 40 · 2021: 61 · 2020: 43 · 2019: 34 · 2018: 19
- FWCI
- 29.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Molecular Informatics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by-nc)
- ETH Zürich Research Collection
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (177477)
- Eidgenössische Technische Hochschule Zürich (16-2 FEL-07)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- de novo molecular design, generative AI, deep learning, recurrent neural networks, SMILES, transfer learning, retinoid X receptor agonist, peroxisome proliferator-activated receptor agonists, drug-like molecules, bioactive compounds, receptor modulation, compound synthesis
- MeSH terms
- Deep Learning, Humans, Drug Design, Quantitative Structure-Activity Relationship, Retinoid X Receptors, Peroxisome Proliferator-Activated Receptors, Small Molecule Libraries, HEK293 Cells, Molecular Docking Simulation
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