{"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-dual-value","title":"Retrosynthetic Planning with Dual Value Networks","arxiv_id":"2301.13755","date":"2023-01-31","proceeding":null,"authors":["Guoqing Liu","Di Xue","Shufang Xie","Yingce Xia","Austin Tripp","Krzysztof Maziarz","Marwin Segler","Tao Qin","Zongzhang Zhang","Tie-Yan Liu"],"abstract":"Retrosynthesis, which aims to find a route to synthesize a target molecule from commercially available starting materials, is a critical task in drug discovery and materials design. Recently, the combination of ML-based single-step reaction predictors with multi-step planners has led to promising results. However, the single-step predictors are mostly trained offline to optimize the single-step accuracy, without considering complete routes. Here, we leverage reinforcement learning (RL) to improve the single-step predictor, by using a tree-shaped MDP to optimize complete routes. Specifically, we propose a novel online training algorithm, called Planning with Dual Value Networks (PDVN), which alternates between the planning phase and updating phase. In PDVN, we construct two separate value networks to predict the synthesizability and cost of molecules, respectively. To maintain the single-step accuracy, we design a two-branch network structure for the single-step predictor. On the widely-used USPTO dataset, our PDVN algorithm improves the search success rate of existing multi-step planners (e.g., increasing the success rate from 85.79% to 98.95% for Retro*, and reducing the number of model calls by half while solving 99.47% molecules for RetroGraph). Additionally, PDVN helps find shorter synthesis routes (e.g., reducing the average route length from 5.76 to 4.83 for Retro*, and from 5.63 to 4.78 for RetroGraph). Our code is available at \\url{https://github.com/DiXue98/PDVN}.","url_abs":"https://arxiv.org/abs/2301.13755v3","url_pdf":"https://arxiv.org/pdf/2301.13755v3.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-dual-value","repo_url":"https://github.com/DiXue98/PDVN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"multi-step-retrosynthesis","task_name":"Multi-step retrosynthesis"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"retrosynthesis","task_name":"Retrosynthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-step-retrosynthesis-on-uspto-190","task":"Multi-step retrosynthesis","dataset":"USPTO-190","model":"PDVN","rank_in_archive_order":1,"of":5,"metrics":{"Success Rate (100 model calls)":"96.84","Success Rate (500 model calls)":"99.47"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2301.13755","atlas_url":"https://app.syntology.ai/?focus=2301.13755","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.13755"}},"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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