The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis
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, 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382
How many drug targets are there?
Overington, J. P., Al-Lazikani, B., & Hopkins, A. L. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd2199 · Cited by 3,505
A quantitative analysis of kinase inhibitor selectivity
Karaman, M. W., Herrgard, S., Treiber, D. K., Gallant, P., Atteridge, C. E., Campbell, B. T., Chan, K. W., Ciceri, P., Davis, M. I., Edeen, P. T., et al. (2008) · Nature Biotechnology · doi:10.1038/nbt1358 · Cited by 2,466
Comprehensive analysis of kinase inhibitor selectivity
Davis, M. I., Hunt, J. P., Herrgard, S., Ciceri, P., Wodicka, L. M., Pallares, G., Hocker, M., Treiber, D. K., & Zarrinkar, P. P. (2011) · Nature Biotechnology · doi:10.1038/nbt.1990 · Cited by 2,409
Targeting the cancer kinome through polypharmacology
Knight, Z. A., Lin, H., & Shokat, K. M. (2010) · Nature reviews. Cancer · doi:10.1038/nrc2787 · Cited by 674
Activation of ERBB2 signaling causes resistance to the EGFR-directed therapeutic antibody cetuximab
Yonesaka, K., Zejnullahu, K., Okamoto, I., Satoh, T., Cappuzzo, F., Souglakos, J., Ercan, D., Rogers, A., Roncalli, M., Takeda, M., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002442 · Cited by 663
Cited by
Chen, L., Tan, X., Wang, D., Zhong, F., Liu, X., Yang, T., Luo, X., Chen, K., Jiang, H., & Zheng, M. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa524 · Cited by 607
Amorim, A. M. B., Piochi, L. F., Gaspar, A. T., Preto, A. J., Rosário-Ferreira, N., & Moreira, I. S. (2024) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00352 · Cited by 132
Perceiver CPI: a nested cross-attention network for compound–protein interaction prediction
Nguyen, N.-Q., Jang, G., Kim, H., & Kang, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac731 · Cited by 83
Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review
Liao, Q., Zhang, Y., Chu, Y., Ding, Y., Liu, Z., Zhao, X., Wang, Y., Wan, J., Ding, Y., Tiwari, P., et al. (2025) · npj Biomedical Innovations · doi:10.1038/s44385-024-00003-9 · Cited by 82
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts
Karimi, M., Wu, D., Wang, Z., & Shen, Y. (2019) · doi:10.1101/2019.12.28.890103 · Cited by 64
Metadata
- Authors
- Tang, J., Szwajda, A., Shakyawar, S., Xu, T., Hintsanen, P., Wennerberg, K., & Aittokallio, T.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-02-21
- Volume
- 54
- Issue
- 3
- Pages
- 735-743
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2035585923
- Cited by
- 645
- References
- 8
- FWCI
- 8.71
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
