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.

Task Description: Regression. Given the target amino acid sequence/compound SMILES string, predict their binding affinity.

Dataset Statistics: 0.3.2 Update: 117,657 DTI pairs, 2,068 drugs, 229 proteins. Before: 118,036 DTI pairs, 2,068 drugs, 229 proteins.

References:

[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.

[2] Huang, Kexin, et al. “DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction” Bioinformatics.