TY - JOUR
T1 - Making sense of large-scale kinase inhibitor bioactivity data sets
T2 - a comparative and integrative analysis
AU - Tang, Jing
AU - Szwajda, Agnieszka
AU - Shakyawar, Sushil
AU - Xu, Tao
AU - Hintsanen, Petteri
AU - Wennerberg, Krister
AU - Aittokallio, Tero
PY - 2014/3/24
Y1 - 2014/3/24
N2 - 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.
AB - 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.
KW - Animals
KW - Computational Biology/methods
KW - Databases, Factual
KW - Drug Discovery/methods
KW - Humans
KW - Protein Kinase Inhibitors/pharmacology
KW - Protein Kinases/metabolism
U2 - 10.1021/ci400709d
DO - 10.1021/ci400709d
M3 - Journal article
C2 - 24521231
SN - 1549-9596
VL - 54
SP - 735
EP - 743
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 3
ER -