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Generative Models as an Emerging Paradigm in the Chemical Sciences
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Traditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling. Therefore, inverse design methods aim to start from the desired property and optimize a corresponding chemical structure. From a machine learning viewpoint, the inverse design problem can be addressed through so-called generative modeling. Mathematically, discriminative models are defined by learning the probability distribution function of properties given the molecular or material structure. In contrast, a generative model seeks to exploit the joint probability of a chemical species with target characteristics. The overarching idea of generative modeling is to implement a system that produces novel compounds that are expected to have a desired set of chemical features, effectively sidestepping issues found in the forward design process. In this contribution, we overview and critically analyze popular generative algorithms like generative adversarial networks, variational autoencoders, flow, and diffusion models. We highlight key differences between each of the models, provide insights into recent success stories, and discuss outstanding challenges for realizing generative modeling discovered solutions in chemical applications.
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Leguy, J., Cauchy, T., Glavatskikh, M., Duval, B., & Mota, B. D. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00458-z · Cited by 79
Can we predict materials that can be synthesised?
Szczypiński, F., Bennett, S., & Jelfs, K. E. (2020) · Chemical Science · doi:10.1039/d0sc04321d · Cited by 78
Kwon, Y., & Lee, J. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00501-7 · Cited by 64
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular DesignPreprint
Gao, W., Mercado, R., & Coley, C. W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06389 · Cited by 63
Thompson, M. W., Gilmer, J., Matsumoto, R. A., Quach, C. D., Shamaprasad, P., Yang, A. H., Iacovella, C. R., McCabe, C., & Cummings, P. T. (2020) · Molecular Physics · doi:10.1080/00268976.2020.1742938 · Cited by 60
Porwol, L., Kowalski, D. J., Henson, A., Long, D., Bell, N. L., & Cronin, L. (2020) · Angewandte Chemie International Edition · doi:10.1002/anie.202000329 · Cited by 60
Big data: The end of the scientific method?
Sauro Succi, A., & Coveney, P. V. (2019) · UCL Discovery (University College London) · Cited by 56
Chemical space exploration: how genetic algorithms find the needle in the haystack
Henault, E. S., Rasmussen, M. H., & Jensen, J. H. (2020) · PeerJ Physical Chemistry · doi:10.7717/peerj-pchem.11 · Cited by 55
Learning molecular potentials with neural networks
Gökcan, H., & Isayev, O. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1564 · Cited by 51
Self-citationMolGrow: A Graph Normalizing Flow for Hierarchical Molecular Generation
Kuznetsov, M., & Polykovskiy, D. A. (2021) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v35i9.17001 · Cited by 49
Nigam, A., Pollice, R., & Aspuru‐Guzik, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.04011 · Cited by 17
Automatic identification of chemical moieties
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Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learningPreprint
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Cited by
Reinvent 4: Modern AI–driven generative molecule design
Loeffler, H. H., He, J., Tibo, A., Janet, J. P., Voronov, A., Mervin, L. H., & Engkvist, O. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00812-5 · Cited by 319
Industrial Agentic AI and generative modeling in complex systems
Boskabadi, M. R., Cao, Y., Khadem, B., Clements, W., Nevin Gerek, Z., Reuthe, E., Sivaram, A., Savoie, C. J., & Mansouri, S. S. (2025) · Current Opinion in Chemical Engineering · doi:10.1016/j.coche.2025.101150 · Cited by 34
Metadata
- Authors
- Anstine, D. M., & Isayev, O.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2023-04-13
- Volume
- 145
- Issue
- 16
- Pages
- 8736-8750
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W4365442601
- Cited by
- 313
- References
- 112
- FWCI
- 22.09
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
