{"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/retrosynthetic-planning-with-experience","title":"Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search","arxiv_id":"2112.06028","date":"2021-12-11","proceeding":null,"authors":["Siqi Hong","Hankz Hankui Zhuo","Kebing Jin","Guang Shao","Zhanwen Zhou"],"abstract":"In retrosynthetic planning, the huge number of possible routes to synthesize a complex molecule using simple building blocks leads to a combinatorial explosion of possibilities. Even experienced chemists often have difficulty to select the most promising transformations. The current approaches rely on human-defined or machine-trained score functions which have limited chemical knowledge or use expensive estimation methods for guiding. Here we an propose experience-guided Monte Carlo tree search (EG-MCTS) to deal with this problem. Instead of rollout, we build an experience guidance network to learn knowledge from synthetic experiences during the search. Experiments on benchmark USPTO datasets show that, EG-MCTS gains significant improvement over state-of-the-art approaches both in efficiency and effectiveness. In a comparative experiment with the literature, our computer-generated routes mostly matched the reported routes. Routes designed for real drug compounds exhibit the effectiveness of EG-MCTS on assisting chemists performing retrosynthetic analysis.","url_abs":"https://arxiv.org/abs/2112.06028v2","url_pdf":"https://arxiv.org/pdf/2112.06028v2.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":"retrosynthetic-planning-with-experience","repo_url":"https://github.com/jjljkjljk/EG-MCTS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"multi-step-retrosynthesis","task_name":"Multi-step retrosynthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-step-retrosynthesis-on-uspto-190","task":"Multi-step retrosynthesis","dataset":"USPTO-190","model":"EG-MCTS","rank_in_archive_order":3,"of":5,"metrics":{"Success Rate (100 model calls)":"85.79","Success Rate (500 model calls)":"96.84"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2112.06028","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.06028"}},"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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