GuacaMol: Benchmarking Models for de Novo Molecular Design
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De novo design seeks to generate molecules with required property profiles by virtual design-make-test cycles. With the emergence of deep learning and neural generative models in many application areas, models for molecular design based on neural networks appeared recently and show promising results. However, the new models have not been profiled on consistent tasks, and comparative studies to well-established algorithms have only seldom been performed. To standardize the assessment of both classical and neural models for de novo molecular design, we propose an evaluation framework, GuacaMol, based on a suite of standardized benchmarks. The benchmark tasks encompass measuring the fidelity of the models to reproduce the property distribution of the training sets, the ability to generate novel molecules, the exploration and exploitation of chemical space, and a variety of single and multiobjective optimization tasks. The benchmarking open-source Python code and a leaderboard can be found on https://benevolent.ai/guacamol .
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Mature software tools now exist to run generative models on conventional computers, and community-accepted benchmarks enable systematic assessment of generated small molecules considering predicted activity, novelty, validity, and diversity.
“Mature software tools now exist to run generative models on conventional computers,154,157 and community-accepted benchmarks enable the systematic assessment of generated small-molecules, considering not only their predicted activity but also their novelty, validity, and diversity.158,159”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [158]
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MSNovelist: de novo structure generation from mass spectra
Stravs, M. A., Dührkop, K., Böcker, S., & Zamboni, N. (2022) · Nature Methods · doi:10.1038/s41592-022-01486-3 · Cited by 226 · see citing works
Artificial intelligence foundation for therapeutic science
Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., & Zitnik, M. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-01131-2 · Cited by 207 · see citing works
Artificial intelligence in chemistry and drug design
Brown, N., Ertl, P., Lewis, R., Luksch, T., Reker, D., & Schneider, N. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00317-x · Cited by 165 · see citing works
Self-citationArtificial intelligence and machine learning for quantum technologies
Krenn, M., Landgraf, J., Foesel, T., & Marquardt, F. (2023) · Physical Review A · doi:10.1103/physreva.107.010101 · Cited by 162 · see citing works
<i>De Novo</i> Design of New Chemical Entities for SARS-CoV-2 Using Artificial Intelligence
Bung, N., Krishnan, S. R., Bulusu, G., & Roy, A. (2021) · Future Medicinal Chemistry · doi:10.4155/fmc-2020-0262 · Cited by 118 · see citing works
Thomas, M., Smith, R. T., O’Boyle, N. M., de Graaf, C., & Bender, A. (2021) · Journal of Cheminformatics · doi:10.1186/s13321-021-00516-0 · Cited by 78 · see citing works
“TP + FP + TN + TN Model performance and diversity metrics Several metrics were used to assess generative model performance, as used in GuacaMol [78] and MOSES [4] (see Additional file 1).”
MethodNiazi, S. K. (2025) · Pharmaceuticals · doi:10.3390/ph18091271 · Cited by 61 · see citing works
Advancing 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 · see citing works
Toward Automated Simulation Research Workflow through LLM Prompt Engineering Design
Liu, Z., Chai, Y., & Li, J. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c01653 · Cited by 24 · see citing works
Tse, E. G., Aithani, L., Anderson, M., Cardoso-Silva, J., Cincilla, G., Conduit, G. J., Galushka, M., Guan, D., Hallyburton, I., Irwin, B. W. J., et al. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c00313 · Cited by 18 · see citing works
- Authors
- Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C.
- Affiliations
- BenevolentAI (United Kingdom) (GB)
- Corresponding author
- Fiscato, Marco
- Segler, Marwin H.S.
- Vaucher, Alain C.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-03-19
- Volume
- 59
- Issue
- 3
- Pages
- 1096-1108
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2900694120
- Cited by
- 895
- References
- 83
- Citations by year
- 2026: 147 · 2025: 149 · 2024: 131 · 2023: 125 · 2022: 100 · 2021: 135 · 2020: 80 · 2019: 27 · 2017: 1
- FWCI
- 57.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Chemical Information and Modeling (publishedVersion, other-oa)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Keywords
- de novo molecular design, benchmarking framework, chemical space exploration, multi-objective optimization, model evaluation
- MeSH terms
- Deep Learning, Pharmaceutical Preparations, Isomerism, Models, Molecular, Monte Carlo Method, Drug Design, Molecular Structure, Benchmarking, Quantitative Structure-Activity Relationship
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.
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