{"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/retro-learning-retrosynthetic-planning-with","title":"Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search","arxiv_id":"2006.15820","date":"2020-06-29","proceeding":"ICML 2020 1","authors":["Binghong Chen","Chengtao Li","Hanjun Dai","Le Song"],"abstract":"Retrosynthetic planning is a critical task in organic chemistry which identifies a series of reactions that can lead to the synthesis of a target product. The vast number of possible chemical transformations makes the size of the search space very big, and retrosynthetic planning is challenging even for experienced chemists. However, existing methods either require expensive return estimation by rollout with high variance, or optimize for search speed rather than the quality. In this paper, we propose Retro*, a neural-based A*-like algorithm that finds high-quality synthetic routes efficiently. It maintains the search as an AND-OR tree, and learns a neural search bias with off-policy data. Then guided by this neural network, it performs best-first search efficiently during new planning episodes. Experiments on benchmark USPTO datasets show that, our proposed method outperforms existing state-of-the-art with respect to both the success rate and solution quality, while being more efficient at the same time.","url_abs":"https://arxiv.org/abs/2006.15820v1","url_pdf":"https://arxiv.org/pdf/2006.15820v1.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":"retro-learning-retrosynthetic-planning-with","repo_url":"https://github.com/binghong-ml/retro_star","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"multi-step-retrosynthesis","task_name":"Multi-step retrosynthesis"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[{"slug":"uspto-190","name":"USPTO-190","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-step-retrosynthesis-on-uspto-190","task":"Multi-step retrosynthesis","dataset":"USPTO-190","model":"Retro*","rank_in_archive_order":5,"of":5,"metrics":{"Success Rate (100 model calls)":"52.11","Success Rate (500 model calls)":"86.84"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2006.15820","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.15820"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/binghong-ml/retro_star","reach":null}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"84a25a944fe2ed46","entry":"molstar","repo":"binghong-ml/retro_star","repo_kind":"official","path":"retro_star/alg/molstar.py","file_url":"https://github.com/binghong-ml/retro_star/blob/HEAD/retro_star/alg/molstar.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"84a25a944fe2ed46"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}