Comprehensive analysis of kinase inhibitor selectivity

By Mindy I. Davis, Jeremy P Hunt, S. Herrgård, P. Ciceri, L. Wodicka, Gabriel Pallares, M. Hocker, D. K. Treiber, P. Zarrinkar. 2011

2,245 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Pre-training Molecular Graph Representation with 3D Geometry (2021, neutral): “Drug-Target Affinity Davis [14] measures the binding affinities between kinase inhibitors and kinases, scored by the Kd value (kinase dissociation constant).”
DeepDTA: deep drug–target binding affinity prediction (2018, methodology): “We evaluated our proposed model on the Kinase data set by [16] as suggested by [42] to be used as benchmark data set for binding affinity prediction evaluation.”
DeepPurpose: a deep learning library for drug–target interaction prediction (2020, result): “We find that many DeepPurpose models achieve comparable prediction performance on two benchmark datasets, DAVIS [Davis et al., 2011] and KIBA [He et al.”
SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines (2017, methodology): “We used the two large-scale biochemical selectivity assays for clinically relevant kinase inhibitors from the studies by [40, 41] that were already used to evaluate the performance of KronRLS in the original paper [4].”
Deep learning in drug discovery: an integrative review and future challenges (2022, methodology): “The MT-DTI model was tweaked to perfection and assessed using two Davis models Using pre-trained parameters from the 97 million chemicals PubChem (Davis et al. 2011) and (KIBA) (Tang et al.”
STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data (2015, background): “Other large sources of protein–chemical links are the datasets of experimentally validated interactions, which include ChEMBL (27), PDSP K i Database (28), Protein Data Bank (PDB) (29) and two high-throughput kinase–ligand interactions studies (30,31).”
Developing irreversible inhibitors of the protein kinase cysteinome (2013, neutral): “Kinome-wide selectivity profiling using KinomeScan methodology demonstrated HKI-272 to be a very selective inhibitor: in addition to EGFR and Her2, it only exhibited potent binding to MAP4K3/5 and MST3/4 (Kd 10 nM) (Davis et al., 2011).”
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development (2021, background): “It is from Davis et al. (2011) and consists of 27,621 pairs.”
Ligand–receptor interaction platforms and their applications for drug discovery (2012, background): “Polypharmacology is concerned with the ability of a drug binding to multiple targets, and recent studies have found that many drugs such as kinase inhibitor drugs display polypharmacology [177-181].”
VEGFA-VEGFR pathway blockade inhibits tumor-induced regulatory T-cell proliferation in colorectal cancer. (2013, background): “Masitinib is currently the most selective TKI in clinical development or already approved to date (20, 21).”
KinMap: a web-based tool for interactive navigation through human kinome data (2017, methodology): “…structures were obtained from the Protein Data Bank [11], the number of assay and compound values from ChEMBL [12], inhibition profiles from published panels [4, 13], and information related to kinase-disease associations from the Center for Therapeutic Target Validation (CTTV) platform [14].”
STITCH 4: integration of protein–chemical interactions with user data (2013, methodology): “We converted the reported residual kinase activities (10) and kinase afﬁnities (11) to probabilistic scores, which gave rise to 14 187, 9431 and 5977 interactions of at least low, medium and high conﬁdence, respectively.”