{"url":"/dataset/kiba","name":"KIBA","full_name":null,"description_markdown":"Dataset Description: Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), Tang et al. introduces a model-based integration approach, termed KIBA to generate an integrated drug-target bioactivity matrix.\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: 117,657 DTI pairs, 2,068 drugs, 229 proteins. Before: 118,036 DTI pairs, 2,068 drugs, 229 proteins.\r\n\r\nReferences:\r\n\r\n[1] Tang J, Szwajda A, Shakyawar S, et al. Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J Chem Inf Model. 2014;54(3):735-743.\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":"Drug Discovery","url":"/task/drug-discovery","datasets_with_task":"/datasets/task/drug-discovery"}],"languages":[],"variants":["KIBA"],"data_loaders":[],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/drug-discovery-on-kiba","task":"Drug Discovery","dataset_variant":"KIBA","rows":4,"metrics":["CI","MSE"],"first_row_in_archive_order":{"model":"SMT-DTA","paper":"/paper/smt-dta-improving-drug-target-affinity","metrics":{"CI":"0.894","MSE":"0.154"},"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"}],"papers_with_a_benchmark_row":[{"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."}