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Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis

Jing Tang, Agnieszka Szwajda, Sushil Shakyawar, Tao Xu, Petteri Hintsanen, Krister Wennerberg, Tero Aittokallio, Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis. Journal of Chemical Information and Modeling 54(3), 735–743, 2014.

Abstract:

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, Ki
, and Kd, 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 databasereported
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.

BibTeX entry:

@ARTICLE{jTaSzShXuHiWeAi14a,
  title = {Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis},
  author = {Tang, Jing and Szwajda, Agnieszka and Shakyawar, Sushil and Xu, Tao and Hintsanen, Petteri and Wennerberg, Krister and Aittokallio, Tero},
  journal = {Journal of Chemical Information and Modeling},
  volume = {54},
  number = {3},
  pages = {735–743},
  year = {2014},
}

Belongs to TUCS Research Unit(s): Biomathematics Research Unit (BIOMATH)

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