{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/molecular-sets-moses-a-benchmarking-platform","title":"Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models","arxiv_id":"1811.12823","date":"2018-11-29","proceeding":null,"authors":["Daniil Polykovskiy","Alexander Zhebrak","Benjamin Sanchez-Lengeling","Sergey Golovanov","Oktai Tatanov","Stanislav Belyaev","Rauf Kurbanov","Aleksey Artamonov","Vladimir Aladinskiy","Mark Veselov","Artur Kadurin","Simon Johansson","Hongming Chen","Sergey Nikolenko","Alan Aspuru-Guzik","Alex Zhavoronkov"],"abstract":"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-supervised 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 a 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.","url_abs":"https://arxiv.org/abs/1811.12823v5","url_pdf":"https://arxiv.org/pdf/1811.12823v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"molecular-sets-moses-a-benchmarking-platform","repo_url":"https://github.com/molecularsets/moses","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"molecular-sets-moses-a-benchmarking-platform","repo_url":"https://github.com/aclyde11/RNNGenerator","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"molecular-sets-moses-a-benchmarking-platform","repo_url":"https://github.com/tiger-tiger/MOSES-2020","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"molecular-graph-generation","task_name":"Molecular Graph Generation"}],"methods":[],"datasets_introduced":[{"slug":"moses","name":"MOSES","full_name":"Molecular sets (MOSES)"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.12823","atlas_url":"https://app.syntology.ai/?focus=1811.12823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.12823"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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