{"url":"/dataset/qm7","name":"QM7","full_name":null,"description_markdown":"QM7 dataset is a subset of the GDB-13 database. GDB-13 contains nearly 1 billion stable and synthetically accessible organic molecules. In the QM7 subset, only molecules with up to 23 atoms are included. These atoms consist of carbon ©, nitrogen (N), oxygen (O), and sulfur (S). The total number of molecules in the QM7 dataset is 7165. Each molecule is represented using the Coulomb matrix, which captures the interactions between atoms.","description_withheld":null,"homepage":"http://quantum-machine.org/datasets/","introduced_date":"2020-06-26","introduced_date_note":null,"introduced_by":{"paper":null,"title":"QM7-X: A comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules","first_author":null,"url":null},"license":null,"modalities":[],"tasks":[{"name":"Molecular Property Prediction","url":"/task/molecular-property-prediction","datasets_with_task":"/datasets/task/molecular-property-prediction"}],"languages":[],"variants":["QM7"],"data_loaders":[],"num_papers_in_archive":24,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/molecular-property-prediction-on-qm7","task":"Molecular Property Prediction","dataset_variant":"QM7","rows":8,"metrics":["MAE"],"first_row_in_archive_order":{"model":"Uni-Mol","paper":"/paper/uni-mol-a-universal-3d-molecular","metrics":{"MAE":"41.8"},"code_links":[{"title":"dptech-corp/Uni-Mol","url":"https://github.com/dptech-corp/Uni-Mol"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/uni-mol-a-universal-3d-molecular","title":"Uni-Mol: A Universal 3D Molecular Representation Learning Framework","date":"2022-09-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/chemrl-gem-geometry-enhanced-molecular","title":"ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction","date":"2021-06-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/grover-self-supervised-message-passing","title":"Self-Supervised Graph Transformer on Large-Scale Molecular Data","date":"2020-06-18","rows_on_this_dataset":2,"code_links":3,"syntology":null},{"paper":"/paper/pre-training-graph-neural-networks","title":"Strategies for Pre-training Graph Neural Networks","date":"2019-05-29","rows_on_this_dataset":1,"code_links":11,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":10,"samples_unverified":4,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/are-learned-molecular-representations-ready","title":"Analyzing Learned Molecular Representations for Property Prediction","date":"2019-04-02","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":3,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/n-gram-graph-a-novel-molecule-representation","title":"N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules","date":"2018-06-24","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":0,"samples_unverified":14,"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":3,"samples_harvested":43,"samples_ran":13,"samples_unverified":30,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":1,"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."}