{"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/generating-focussed-molecule-libraries-for","title":"Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks","arxiv_id":"1701.01329","date":"2017-01-05","proceeding":null,"authors":["Marwin H. S. Segler","Thierry Kogej","Christian Tyrchan","Mark P. Waller"],"abstract":"In de novo drug design, computational strategies are used to generate novel\nmolecules with good affinity to the desired biological target. In this work, we\nshow that recurrent neural networks can be trained as generative models for\nmolecular structures, similar to statistical language models in natural\nlanguage processing. We demonstrate that the properties of the generated\nmolecules correlate very well with the properties of the molecules used to\ntrain the model. In order to enrich libraries with molecules active towards a\ngiven biological target, we propose to fine-tune the model with small sets of\nmolecules, which are known to be active against that target.\n  Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test\nmolecules that medicinal chemists designed, whereas against Plasmodium\nfalciparum (Malaria) it reproduced 28% of 1240 test molecules. When coupled\nwith a scoring function, our model can perform the complete de novo drug design\ncycle to generate large sets of novel molecules for drug discovery.","url_abs":"http://arxiv.org/abs/1701.01329v1","url_pdf":"http://arxiv.org/pdf/1701.01329v1.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":"generating-focussed-molecule-libraries-for","repo_url":"https://github.com/benevolentAI/guacamol_baselines","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"generating-focussed-molecule-libraries-for","repo_url":"https://github.com/cool21th/ai_drug_discovery","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"generating-focussed-molecule-libraries-for","repo_url":"https://github.com/jaechanglim/GFML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"generating-focussed-molecule-libraries-for","repo_url":"https://github.com/jaechanglim/molecule-generator","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"generating-focussed-molecule-libraries-for","repo_url":"https://github.com/sanjaradylov/moleculegen-ml","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"drug-design","task_name":"Drug Design"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1701.01329","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1701.01329"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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