{"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/guacamol-benchmarking-models-for-de-novo","title":"GuacaMol: Benchmarking Models for De Novo Molecular Design","arxiv_id":"1811.09621","date":"2018-11-22","proceeding":null,"authors":["Nathan Brown","Marco Fiscato","Marwin H. S. Segler","Alain C. Vaucher"],"abstract":"De novo design seeks to generate molecules with required property profiles by\nvirtual design-make-test cycles. With the emergence of deep learning and neural\ngenerative models in many application areas, models for molecular design based\non neural networks appeared recently and show promising results. However, the\nnew models have not been profiled on consistent tasks, and comparative studies\nto well-established algorithms have only seldom been performed.\n  To standardize the assessment of both classical and neural models for de novo\nmolecular design, we propose an evaluation framework, GuacaMol, based on a\nsuite of standardized benchmarks. The benchmark tasks encompass measuring the\nfidelity of the models to reproduce the property distribution of the training\nsets, the ability to generate novel molecules, the exploration and exploitation\nof chemical space, and a variety of single and multi-objective optimization\ntasks. The benchmarking open-source Python code, and a leaderboard can be found\non https://benevolent.ai/guacamol","url_abs":"http://arxiv.org/abs/1811.09621v2","url_pdf":"http://arxiv.org/pdf/1811.09621v2.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":"guacamol-benchmarking-models-for-de-novo","repo_url":"https://github.com/benevolentAI/guacamol","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"guacamol-benchmarking-models-for-de-novo","repo_url":"https://github.com/benevolentAI/guacamol_baselines","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"guacamol-benchmarking-models-for-de-novo","repo_url":"https://github.com/machinelearninglifescience/poli","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"molecular-property-prediction","task_name":"Molecular Property Prediction"}],"methods":[],"datasets_introduced":[{"slug":"guacamol","name":"Guacamol","full_name":"Guacamol"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.09621","atlas_url":"https://app.syntology.ai/?focus=1811.09621","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.09621"}},"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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