Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Open accessAbstract
Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervized predictive models in the downstream tasks. While there are plenty of generative models, it is unclear how to compare and rank them. In this work, we introduce a benchmarking platform called Molecular Sets (MOSES) to standardize training and comparison of molecular generative models. MOSES provides training and testing datasets, and a set of metrics to evaluate the quality and diversity of generated structures. We have implemented and compared several molecular generation models and suggest to use our results as reference points for further advancements in generative chemistry research. The platform and source code are available at https://github.com/molecularsets/moses .
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The MOSES dataset comprises 1,936,962 molecular structures, divided into training, testing, and scaffold datasets containing 1.6 million, 176,000, and 176,000 molecules respectively.
“The MOSES dataset comprises a total of 1,936,962 molecular structures, which are subsequently divided into training, testing, and scaffold datasets, containing 1.6 million, 176,000, and 176,000 molecules respectively.”
From PCF-VAE: posterior collapse free variational autoencoder for de novo drug design (2025) · cites this paper as [32]
The Dataset section reports three non-overlapping splits of 1,584,664 training, 176,075 test, and 176,226 scaffold-test molecules, supporting the rounded split sizes. Those counts sum to 1,936,965, three more than the claimed total of 1,936,962; the total is not stated in the available passage. (LLM, conf=0.90)
Experimental — accuracy not yet measuredMature 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 [159]
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“We (1) first removed known DRD2 actives (according to the ExCAPE-DB [58]) from the MOSES curated [4] ZINC [59] database of small drug-like molecules for use as training data.”
BackgroundMethodInfluentialAdvancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
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- Authors
- Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., Kadurin, A., Johansson, S., Chen, H., Nikolenko, S., Aspuru-Guzik, A., & Zhavoronkov, A.
- Affiliations
- AstraZeneca (Sweden) (SE)
- National Research University Higher School of Economics (RU)
- Corresponding author
- Polykovskiy, Daniil
- Nikolenko, Sergey
- Zhavoronkov, Alex
- ORCID
- Venue
- Frontiers in Pharmacology
- Publisher
- Frontiers Media SA
- Published
- 2020-12-18
- Volume
- 11
- Pages
- 565644-565644
- ISSN
- 1663-9812 (ISSN-L)
- PMC
- PMC7775580
- OpenAlex
- W2903425689
- Cited by
- 849
- References
- 108
- Citations by year
- 2026: 159 · 2025: 174 · 2024: 159 · 2023: 118 · 2022: 75 · 2021: 103 · 2020: 46 · 2019: 14 · 2017: 1
- FWCI
- 42.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Pharmacology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Globus Services
- 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
- molecular generative models, benchmarking platform, Moses, molecular generation, generative chemistry, model comparison, evaluation metrics, virtual screening, molecular property prediction
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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