We carried out a systematic evaluation of target selectivity profiles across three recent large-scale biochemical assays of kinase inhibitors and further compared these standardized bioactivity assays with data reported in the widely used databases ChEMBL and STITCH. Our comparative evaluation revealed relative benefits and potential limitations among the bioactivity types, as well as pinpointed biases in the database curation processes. Ignoring such issues in data heterogeneity and representation may lead to biased modeling of drugs' polypharmacological effects as well as to unrealistic evaluation of computational strategies for the prediction of drug-target interaction networks. Toward making use of the complementary information captured by the various bioactivity types, including IC50, K(i), and K(d), we also introduce a model-based integration approach, termed KIBA, and demonstrate here how it can be used to classify kinase inhibitor targets and to pinpoint potential errors in database-reported drug-target interactions. An integrated drug-target bioactivity matrix across 52,498 chemical compounds and 467 kinase targets, including a total of 246,088 KIBA scores, has been made freely available.
Citation: Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J Chem Inf Model. 2014 Mar 24;54(3):735-43. doi: 10.1021/ci400709d.
https://pubmed.ncbi.nlm.nih.gov/24521231/
Data file download: https://www2.helsinki.fi/en/researchgroups/network-pharmacology-for-precision-medicine/software#section-106744