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The Advent of Generative Chemistry
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Generative adversarial networks (GANs), first published in 2014, are among the most important concepts in modern artificial intelligence (AI). Bridging deep learning and game theory, GANs are used to generate or “imagine” new objects with desired properties. Since 2016, multiple GANs with reinforcement learning (RL) have been successfully applied in pharmacology for de novo molecular design. Those techniques aim at a more efficient use of the data and a better exploration of the chemical space. We review recent advances for the generation of novel molecules with desired properties with a focus on the applications of GANs, RL, and related techniques. We also discuss the current limitations and challenges in the new growing field of generative chemistry.
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“The development of ultra-large chemical libraries (94) and generative chemistry methods, particularly those based on Generative Adversarial Networks (GANs), have significantly expanded the possibilities for hit generation and drug discovery.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [94]
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
A Brief Survey of Deep Reinforcement Learning
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · arXiv · doi:10.48550/arxiv.1708.05866 · Cited by 4,733
Deep Reinforcement Learning: A Brief Survey
Arulkumaran, K., Deisenroth, M. P., Brundage, M., & Bharath, A. A. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2743240 · Cited by 4,733
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
Generative Adversarial Networks
Drori, I. (2022) · Cambridge University Press eBooks · doi:10.1017/9781108891530.013 · Cited by 2,557
Quantifying the chemical beauty of drugs
Bickerton, G. R., Paolini, G. V., Besnard, J., Mureşan, S., & Hopkins, A. L. (2012) · Nature Chemistry · doi:10.1038/nchem.1243 · Cited by 2,397
Inverse molecular design using machine learning: Generative models for matter engineering
Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,177
QSAR Modeling: Where have you been? Where are you going to?
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4004285 · Cited by 2,164
NIPS 2016 Tutorial: Generative Adversarial NetworksPreprint
Goodfellow, I. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.00160 · Cited by 2,048
Research works printed but not linked: 1
- Maziarka, Ł.; et al. Mol-CycleGAN - a generative model for molecular optimization. arXiv (Machine Learning), February 6, 2019, 1902.02119, ver. 1. https://arxiv.org/abs/1902.02119. · arXiv
Cited by
Sorted by citations
Computational approaches streamlining drug discovery
Sadybekov, A., & Katritch, V. (2023) · Nature · doi:10.1038/s41586-023-05905-z · Cited by 1,424
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Artificial intelligence in drug development
Zhang, K., Yang, X., Wang, Y., Yu, Y., Huang, N., Li, G. L., Li, X., Wu, J. C., & Yang, S. (2025) · Nature Medicine · doi:10.1038/s41591-024-03434-4 · Cited by 585
Artificial intelligence in drug discovery: recent advances and future perspectives
Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1909567 · Cited by 464
Jackson, I., Ivanov, D., Dolgui, A. B., & Namdar, J. (2024) · International Journal of Production Research · doi:10.1080/00207543.2024.2309309 · Cited by 567
Ren, F., Ding, X., Zheng, M., Korzinkin, M., Cai, X., Zhu, W., Mantsyzov, A., Aliper, A., Aladinskiy, V., Cao, Z., et al. (2023) · Chemical Science · doi:10.1039/d2sc05709c · Cited by 365
Self-citationAdvances in De Novo Drug Design: From Conventional to Machine Learning Methods
Mouchlis, V. D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A. G., Aidinis, V., Lynch, I., Greco, D., & Melagraki, G. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22041676 · Cited by 381
A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models
Ren, F., Aliper, A., Chen, J., Zhao, H., Rao, S., Kuppe, C., Ozerov, I. V., Zhang, M., Witte, K., Kruse, C., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-024-02143-0 · Cited by 348
Self-citationGenerative models for molecular discovery: Recent advances and challenges
Bilodeau, C., Jin, W., Jaakkola, T., Barzilay, R., & Jensen, K. F. (2022) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1608 · Cited by 359
Machine learning in materials science: From explainable predictions to autonomous design
Pilania, G. (2021) · Computational Materials Science · doi:10.1016/j.commatsci.2021.110360 · Cited by 298
Chemistry42: 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
Self-citationArtificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236
“techniques, such as generative chemistry (93).”
The Evolution of Data-Driven Modeling in Organic Chemistry
Williams, W. L., Zeng, L., Gensch, T., Sigman, M. S., Doyle, A. G., & Anslyn, E. V. (2021) · ACS Central Science · doi:10.1021/acscentsci.1c00535 · Cited by 193
Enhancing preclinical drug discovery with artificial intelligence
Vijayan, R., Kihlberg, J. O., Cross, J. B., & Poongavanam, V. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.11.023 · Cited by 191
Deep learning tools for advancing drug discovery and development
Nag, S., Baidya, A. T. K., Mandal, A., Mathew, A. T., Das, B., Devi, B., & Kumar, R. (2022) · 3 Biotech · doi:10.1007/s13205-022-03165-8 · Cited by 185
Chemical language models enable navigation in sparsely populated chemical space
Skinnider, M. A., Stacey, R. G., Wishart, D. S., & Foster, L. J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00368-1 · Cited by 170
Comprehensive Survey of Recent Drug Discovery Using Deep Learning
Kim, J., Park, S., Min, D., & Kim, W. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22189983 · Cited by 147
Gangwal, A., Ansari, A., Ahmad, I., Azad, A. K., & Sulaiman, W. M. A. W. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108734 · Cited by 145
Defining and Exploring Chemical Spaces
Coley, C. W. (2020) · Trends in Chemistry · doi:10.1016/j.trechm.2020.11.004 · Cited by 134
Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation
Fuhr, A., & Sumpter, B. G. (2022) · Frontiers in Materials · doi:10.3389/fmats.2022.865270 · Cited by 128
Artificial Intelligence and Advanced Materials
López, C. (2022) · Advanced Materials · doi:10.1002/adma.202208683 · Cited by 123
Pun, F. W., Liu, B. H. M., Long, X., Leung, H. W., Leung, G. H. D., Mewborne, Q. T., Gao, J., Shneyderman, A., Ozerov, I. V., Wang, J., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.914017 · Cited by 115
“The advance of generative adversarial networks (GANs) accelerated the process of target discovery using transcriptomic data and de novo molecular design (Aliper et al., 2016; West et al., 2018; Vanhaelen et al., 2020).”
MethodSelf-citationCritical assessment of AI in drug discovery
Walters, W. P., & Barzilay, R. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1915982 · Cited by 113
GenAICHI: Generative AI and HCI
Muller, M., Chilton, L. B., Kantosalo, A., Martin, C. P., & Walsh, G. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3503719 · Cited by 101
Talat, A., & Khan, A. U. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103491 · Cited by 96
Introduction to small molecule drug discovery and preclinical development
Southey, M. W. Y., & Brunavs, M. (2023) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2023.1314077 · Cited by 106
Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry
Kao, P.-Y., Yang, Y.-C., Chiang, W.-Y., Hsiao, J.-Y., Cao, Y., Aliper, A., Ren, F., Aspuru‐Guzik, A., Zhavoronkov, A. A., Hsieh, M.-H., et al. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00562 · Cited by 109
Self-citationUsing GANs with adaptive training data to search for new molecules
Blanchard, A. E., Stanley, C. B., & Bhowmik, D. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00494-3 · Cited by 87
De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update
Lin, E., Lin, C., & Lane, H. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01361 · Cited by 76
Li, J., Zhao, T. J., Yang, Q., Du, S., & Xu, L. (2024) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2024.105278 · Cited by 84
Invalid SMILES are beneficial rather than detrimental to chemical language models
Skinnider, M. A. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00821-x · Cited by 80
Tripathi, S., Augustin, A. I., Dunlop, A., Sukumaran, R., Dheer, S., Zavalny, A., Haslam, O., Austin, T. C., Donchez, J., Tripathi, P. K., et al. (2022) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2022.100045 · Cited by 67
Design of potent antimalarials with generative chemistry
Godinez, W. J., Ma, E. J., Chao, A. T., Pei, L., Skewes-Cox, P., Canham, S. M., Jenkins, J. L., Young, J. M., Martin, É., & Guiguemde, W. A. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00448-w · Cited by 57
Kell, D. B., Samanta, S., & Swainston, N. (2020) · Biochemical Journal · doi:10.1042/bcj20200781 · Cited by 58
Generative adversarial networks for transition state geometry prediction
Makoś, M. Z., Verma, N., Larson, E. C., Freindorf, M., & Kraka, E. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0055094 · Cited by 54
Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry
Mroz, A. M., Basford, A. R., Hastedt, F., Jayasekera, I. S., Mosquera‐Lois, I., Sedgwick, R., Ballester, P. J., Bocarsly, J. D., del Rio‐Chanona, E. A., Evans, M. L., et al. (2025) · Chemical Society Reviews · doi:10.1039/d5cs00146c · Cited by 61
Delgado‐Licona, F., & Abolhasani, M. (2022) · Advanced Intelligent Systems · doi:10.1002/aisy.202200331 · Cited by 55
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Ivanenkov, Y. A., Zagribelnyy, B. A., Малышев, А. В., Evteev, S. A., Terentiev, V. A., Kamya, P. R. N., Bezrukov, D. S., Aliper, A., Ren, F., & Zhavoronkov, A. A. (2023) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.3c00041 · Cited by 57
Self-citationAdvancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50
Franceschelli, G., & Musolesi, M. (2024) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.15278 · Cited by 54
Genetic Optimization of Homogeneous Catalysts
Laplaza, R., Gallarati, S., & Corminbœuf, C. (2022) · Chemistry - Methods · doi:10.1002/cmtd.202100107 · Cited by 48
Yu, Y., Huang, J., He, H., Han, J., Ye, G., Xu, T., Sun, X., Chen, X., Ren, X., Li, C., et al. (2023) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.2c00515 · Cited by 44
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Goldman, B. B., Kearnes, S. M., Kramer, T. J., Riley, P., & Walters, W. P. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00334 · Cited by 45
A systematic review of deep learning chemical language models in recent era
Flores-Hernandez, H., & Martínez-Ledesma, E. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00916-y · Cited by 52
Wang, Y., Wang, C., Liu, T., Qi, H., Chen, S., Cai, X., Zhang, M., Aliper, A., Ren, F., Ding, X., et al. (2023) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.3c01476 · Cited by 39
Self-citationNnadili, M., Okafor, A. N., Olayiwola, T., Akinpelu, D. A., Kumar, R., & Romagnoli, J. A. (2024) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.4c00401 · Cited by 40
Liu, Y., Vijlder, T. D., Bittremieux, W., Laukens, K., & Heyndrickx, W. (2021) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.9120 · Cited by 35
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Liu, Y., Zhang, L., Jiang, Z., Tian, X., Li, P., Wu, P., Du, W., Yuan, B., Xie, C., Bu, G., et al. (2025) · MedComm · doi:10.1002/mco2.70317 · Cited by 42
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Born, J., & Manica, M. (2021) · Current Medicinal Chemistry · doi:10.2174/0929867328666210729115728 · Cited by 36
Bian, Y., & Xie, X. (2022) · Cells · doi:10.3390/cells11050915 · Cited by 32
Language models for the prediction of SARS-CoV-2 inhibitors
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Born, J., Huynh, T., Stroobants, A., Cornell, W. D., & Manica, M. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00889 · Cited by 25
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Roberts, J., Bursten, J. R. S., & Risko, C. M. (2021) · Chemistry of Materials · doi:10.1021/acs.chemmater.1c00538 · Cited by 24
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Steurer, B., Vanhaelen, Q., & Zhavoronkov, A. A. (2024) · The Journals of Gerontology Series A · doi:10.1093/gerona/glae006 · Cited by 16
Self-citationLu, H., Sun, D., Wang, Z., Cui, H., Min, L., Zhang, H., Zhang, Y., Wu, J., Cai, X., Ding, X., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c01616 · Cited by 17
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Javid, S., Rahmanulla, A., Ahmed, M. G., Sultana, R., & Kumar, B. R. P. (2025) · Intelligent Pharmacy · doi:10.1016/j.ipha.2024.11.003 · Cited by 18
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Reboul, E., Wefers, Z., Prabakaran, H., Waldispühl, J., & Taly, A. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02261 · Cited by 13
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Bechelli, S., & Delhommelle, J. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100038 · Cited by 15
Liu, B. H. M., Lin, Y., Long, X., Hung, S. W., Gaponova, A. V., Ren, F., Zhavoronkov, A. A., Pun, F. W., & Wang, C. C. (2024) · Advanced Science · doi:10.1002/advs.202406565 · Cited by 16
Self-citationZhang, M., Ding, X., Cao, Z., Yang, Y., Ding, X., Cai, X., Zhang, M., Aliper, A. M., Ren, F., Lu, H., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.4c02732 · Cited by 12
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Meng, F., Liu, J., Cao, Z., Yu, J., Steurer, B., Yang, Y., Wang, Y., Cai, X., Zhang, M., Ren, F., et al. (2024) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2024.107285 · Cited by 10
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Self-citationZhao Qin, A., & Destree, A. P. (2024) · Advanced Materials · doi:10.1002/adma.202414970 · Cited by 10
Chemistry42: An AI-based platform for de novo molecular designPreprint
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Garcia, A. A., Candello, H. C. D. S. P., Badillo-Urquiola, K. A., & Wong-Villacrés, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714069 · Cited by 15
Das, A., Nandi, A., Kumari, V., & Alvala, M. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815179934123010008 · Cited by 7
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An AI-Driven Framework for Discovery of BACE1 Inhibitors for Alzheimer’s DiseasePreprint
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Goal-directed molecule generation with fine-tuning by policy gradient
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Strategic partnerships for AI-driven drug discovery: The role of relational dynamics
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Comparing Molecules Generated by MMPDB and REINVENT4 with Ideas from Drug Discovery Design Teams
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PROTACs in targeted protein degradation: Advances in development and AI-enhanced drug discovery
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Configurable soft computing-based generative model: The search for catalytic peptides
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PDE4D inhibitors: Opening a new era of PET diagnostics for Alzheimer's disease
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Artificial Intelligence and Machine Learning in Drug Discovery
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CDGCN: Conditional de novo Drug Generative Model Using Graph Convolution Networks
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Metadata
- Authors
- Vanhaelen, Q., Lin, Y.-C., & Zhavoronkov, A.
- Affiliations
- Insilico Medicine Hong Kong Ltd , Pak Shek Kok , ,
- Corresponding author
- Vanhaelen, Quentin
- Venue
- ACS Medicinal Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-07-14
- Volume
- 11
- Issue
- 8
- Pages
- 1496-1505
- ISSN
- 1948-5875 (ISSN-L)
- PMC
- PMC7429972
- OpenAlex
- W3043461363
- Cited by
- 151
- References
- 76
- Citations by year
- 2026: 18 · 2025: 27 · 2024: 32 · 2023: 26 · 2022: 16 · 2021: 30 · 2020: 2
- FWCI
- 9.19
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- ACS Medicinal Chemistry Letters (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- generative chemistry, generative adversarial networks, reinforcement learning, de novo molecular design, drug discovery, chemical space exploration, molecular property prediction, generative models
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