{"url":"/dataset/uspto-190","name":"USPTO-190","full_name":null,"description_markdown":"A chemical synthesis route dataset constructed from the USPTO reaction dataset (1976-Sep2016) and a list of commercially available\r\nbuilding blocks from eMolecules  (~23.1M molecules).  After processing, the dataset has 299202 training routes, 65274 validation routes, 190 test routes, and the corresponding target molecules.","description_withheld":null,"homepage":"","introduced_date":"2020-06-29","introduced_date_note":null,"introduced_by":{"paper":"/paper/retro-learning-retrosynthetic-planning-with","title":"Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search","first_author":"Binghong Chen","url":null},"license":null,"modalities":[],"tasks":[{"name":"Multi-step retrosynthesis","url":"/task/multi-step-retrosynthesis","datasets_with_task":"/datasets/task/multi-step-retrosynthesis"}],"languages":[],"variants":["USPTO-190"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multi-step-retrosynthesis-on-uspto-190","task":"Multi-step retrosynthesis","dataset_variant":"USPTO-190","rows":5,"metrics":["Success Rate (100 model calls)","Success Rate (500 model calls)"],"first_row_in_archive_order":{"model":"PDVN","paper":"/paper/retrosynthetic-planning-with-dual-value","metrics":{"Success Rate (100 model calls)":"96.84","Success Rate (500 model calls)":"99.47"},"code_links":[{"title":"DiXue98/PDVN","url":"https://github.com/DiXue98/PDVN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/retrosynthetic-planning-with-dual-value","title":"Retrosynthetic Planning with Dual Value Networks","date":"2023-01-31","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":4,"samples_unverified":4,"pointer_only_for_licence":8,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/retrograph-retrosynthetic-planning-with-graph","title":"RetroGraph: Retrosynthetic Planning with Graph Search","date":"2022-06-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/retrosynthetic-planning-with-experience","title":"Retrosynthetic Planning with Experience-Guided Monte Carlo Tree Search","date":"2021-12-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/self-improved-retrosynthetic-planning","title":"Self-Improved Retrosynthetic Planning","date":"2021-06-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":3,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/retro-learning-retrosynthetic-planning-with","title":"Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search","date":"2020-06-29","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":18,"samples_ran":7,"samples_unverified":11,"pointer_only_for_licence":8,"papers_with_no_sample_that_ran":2,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}