{"url":"/dataset/davis-dta","name":"DAVIS-DTA","full_name":null,"description_markdown":"Dataset Description: The interaction of 72 kinase inhibitors with 442 kinases covering >80% of the human catalytic protein kinome.\r\n\r\nTask Description: Regression. Given the target amino acid sequence/compound SMILES string, predict their binding affinity.\r\n\r\nDataset Statistics: 0.3.2 Update: 25,772 DTI pairs, 68 drugs, 379 proteins. Before: 27,621 DTI pairs, 68 drugs, 379 proteins.\r\n\r\n[1] Davis, M., Hunt, J., Herrgard, S. et al. Comprehensive analysis of kinase inhibitor selectivity. Nat Biotechnol 29, 1046–1051 (2011).\r\n\r\n[2] Huang, Kexin, et al. “DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction” Bioinformatics.","description_withheld":null,"homepage":"https://tdcommons.ai/multi_pred_tasks/dti/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"task","url":null,"datasets_with_task":"/datasets/task/task"},{"name":"Drug Discovery","url":"/task/drug-discovery","datasets_with_task":"/datasets/task/drug-discovery"},{"name":"Protein Language Model","url":"/task/protein-language-model","datasets_with_task":"/datasets/task/protein-language-model"}],"languages":[],"variants":["DAVIS-DTA"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/drug-discovery-on-davis-dta","task":"Drug Discovery","dataset_variant":"DAVIS-DTA","rows":4,"metrics":["CI","MSE"],"first_row_in_archive_order":{"model":"SMT-DTA","paper":"/paper/smt-dta-improving-drug-target-affinity","metrics":{"CI":"0.890","MSE":"0.219"},"code_links":[{"title":"qizhipei/ssm-dta","url":"https://github.com/qizhipei/ssm-dta"},{"title":"qizhipei/smt-dta","url":"https://github.com/qizhipei/smt-dta"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/on-davis-dta","task":"","dataset_variant":"DAVIS-DTA","rows":1,"metrics":["MSE"],"first_row_in_archive_order":{"model":"PGraphDTA","paper":"/paper/pgraphdta-improving-drug-target-interaction","metrics":{"MSE":"0.221"},"code_links":[{"title":"yijia-xiao/pgraphdta","url":"https://github.com/yijia-xiao/pgraphdta"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/protein-language-model-on-davis-dta","task":"Protein Language Model","dataset_variant":"DAVIS-DTA","rows":1,"metrics":["CI"],"first_row_in_archive_order":{"model":"LEP-AD","paper":"/paper/lep-ad-language-embedding-of-proteins-and","metrics":{"CI":"89.5"},"code_links":[{"title":"adaga06/LEP-AD","url":"https://github.com/adaga06/LEP-AD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/pgraphdta-improving-drug-target-interaction","title":"PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps","date":"2023-10-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lep-ad-language-embedding-of-proteins-and","title":"LEP-AD: Language Embedding of Proteins and Attention to Drugs predicts drug target interactions","date":"2023-03-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/smt-dta-improving-drug-target-affinity","title":"SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction","date":"2022-06-20","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deeppurpose-a-deep-learning-based-drug","title":"DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction","date":"2020-04-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/graphdta-prediction-of-drugtarget-binding","title":"GraphDTA: prediction of drug–target binding affinity using graph convolutional networks","date":"2019-07-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deepdta-deep-drug-target-binding-affinity","title":"DeepDTA: Deep Drug-Target Binding Affinity Prediction","date":"2018-01-30","rows_on_this_dataset":1,"code_links":4,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"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."}