Tang Jing, Szwajda Agnieszka, Shakyawar Sushil, Xu Tao, Hintsanen Petteri, Wennerberg Krister, Aittokallio Tero
Institute for Molecular Medicine Finland (FIMM), University of Helsinki , Tukholmankatu 8, FI-00290, Helsinki, Finland.
J Chem Inf Model. 2014 Mar 24;54(3):735-43. doi: 10.1021/ci400709d. Epub 2014 Feb 21.
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.
我们对近期三项大规模激酶抑制剂生化检测的靶点选择性概况进行了系统评估，并将这些标准化生物活性检测与广泛使用的数据库ChEMBL和STITCH中报告的数据进行了进一步比较。我们的比较评估揭示了生物活性类型之间的相对优势和潜在局限性，同时也指出了数据库管理过程中的偏差。忽略数据异质性和表示中的此类问题可能会导致对药物多药理学效应的偏差建模，以及对药物-靶点相互作用网络预测的计算策略的不切实际评估。为了利用包括IC50、K(i)和K(d)在内的各种生物活性类型所捕获的互补信息，我们还引入了一种基于模型的整合方法，称为KIBA，并在此展示了它如何用于对激酶抑制剂靶点进行分类以及找出数据库报告的药物-靶点相互作用中的潜在错误。一个涵盖52498种化合物和467个激酶靶点的综合药物-靶点生物活性矩阵，包括总共246088个KIBA分数，已免费提供。
