메뉴 건너뛰기




Volumn 56, Issue 17, 2013, Pages 6991-7002

Selectivity data: Assessment, predictions, concordance, and implications

Author keywords

[No Author keywords available]

Indexed keywords

1 (1 CYANO 1 METHYLETHYL) 3 METHYL 8 (3 QUINOLINYL)IMIDAZO[4,5 C]QUINOLIN 2(1H,3H) ONE; 4 [[9 CHLORO 7 (2,6 DIFLUOROPHENYL) 5H PYRIMIDO[5,4 D][2]BENZAZEPIN 2 YL]AMINO]BENZOIC ACID; 5 (2,6 DICHLOROPHENYL) 2 (2,4 DIFLUOROPHENYLTHIO)PYRIMIDO[1,6 B]PYRIDAZIN 6 ONE; ALISERTIB; AST 487; BARASERTIB; CRIZOTINIB; CYC 116; CYCLIN DEPENDENT KINASE 4; CYCLIN DEPENDENT KINASE 5; CYCLIN DEPENDENT KINASE 6; CYCLIN DEPENDENT KINASE 9; DANUSERTIB; DASATINIB; DORAMAPIMOD; DOVITINIB; ERLOTINIB; FLT3 LIGAND; IMATINIB; JNJ 7706621; LINIFANIB; MK 1175; PALBOCICLIB; PAZOPANIB; PF 04217903; PF 562271; SOTRASTAURIN; TAE 684; TOZASERTIB; UNCLASSIFIED DRUG; VATALANIB;

EID: 84884276795     PISSN: 00222623     EISSN: 15204804     Source Type: Journal    
DOI: 10.1021/jm400798j     Document Type: Article
Times cited : (12)

References (27)
  • 1
    • 77249142404 scopus 로고    scopus 로고
    • The art of the chemical probe
    • Frye, S. V. The art of the chemical probe Nat. Chem. Biol. 2010, 6, 159-161
    • (2010) Nat. Chem. Biol. , vol.6 , pp. 159-161
    • Frye, S.V.1
  • 2
    • 58149102549 scopus 로고    scopus 로고
    • Assessment of chemical coverage of kinome space and its implications for kinase drug discovery
    • Bamborough, P.; Drewry, D.; Harper, G.; Smith, G. K.; Schneider, K. Assessment of chemical coverage of kinome space and its implications for kinase drug discovery J. Med. Chem. 2008, 51, 7898-7914
    • (2008) J. Med. Chem. , vol.51 , pp. 7898-7914
    • Bamborough, P.1    Drewry, D.2    Harper, G.3    Smith, G.K.4    Schneider, K.5
  • 3
    • 33750986884 scopus 로고    scopus 로고
    • "Bayes affinity fingerprints" improve retrieval rates in virtual screening and define orthogonal bioactivity space: When are multitarget drugs a feasible concept?
    • Bender, A.; Jenkins, J. L.; Glick, M.; Deng, Z.; Nettles, J. H.; Davies, J. W. "Bayes affinity fingerprints" improve retrieval rates in virtual screening and define orthogonal bioactivity space: When are multitarget drugs a feasible concept? J. Chem. Inf. Model. 2006, 46, 2445-2456
    • (2006) J. Chem. Inf. Model. , vol.46 , pp. 2445-2456
    • Bender, A.1    Jenkins, J.L.2    Glick, M.3    Deng, Z.4    Nettles, J.H.5    Davies, J.W.6
  • 8
    • 84858033532 scopus 로고    scopus 로고
    • Improved machine learning models for predicting selective compounds
    • Ning, X.; Walters, M.; Karypis, G. Improved machine learning models for predicting selective compounds J. Chem. Inf. Model. 2012, 52, 38-50
    • (2012) J. Chem. Inf. Model. , vol.52 , pp. 38-50
    • Ning, X.1    Walters, M.2    Karypis, G.3
  • 9
    • 80052015102 scopus 로고    scopus 로고
    • Profile-QSAR: A novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity
    • Martin, E.; Mukherjee, P.; Sullivan, D.; Jansen, J. Profile-QSAR: A novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity J. Chem. Inf. Model. 2011, 51, 1942-1956
    • (2011) J. Chem. Inf. Model. , vol.51 , pp. 1942-1956
    • Martin, E.1    Mukherjee, P.2    Sullivan, D.3    Jansen, J.4
  • 10
    • 84866707492 scopus 로고    scopus 로고
    • Profile-QSAR and surrogate AutoShim protein-family modeling of proteases
    • Mukherjee, P.; Martin, E. Profile-QSAR and surrogate AutoShim protein-family modeling of proteases J. Chem. Inf. Model. 2012, 52, 2430-2440
    • (2012) J. Chem. Inf. Model. , vol.52 , pp. 2430-2440
    • Mukherjee, P.1    Martin, E.2
  • 11
    • 84878884695 scopus 로고    scopus 로고
    • What general conclusions can we draw from kinase profiling data sets?
    • Sutherland, J. J.; Gao, C.; Cahya, S.; M., V. What general conclusions can we draw from kinase profiling data sets? Biochim. Biophys. Acta 2013, 1834, 1425-1433
    • (2013) Biochim. Biophys. Acta , vol.1834 , pp. 1425-1433
    • Sutherland, J.J.1    Gao, C.2    Cahya, S.M.V.3
  • 12
    • 84859204703 scopus 로고    scopus 로고
    • Three useful dimensions for domain applicability in QSAR models using random forest
    • Sheridan, R. P. Three useful dimensions for domain applicability in QSAR models using random forest J. Chem. Inf. Model. 2012, 52, 814-823
    • (2012) J. Chem. Inf. Model. , vol.52 , pp. 814-823
    • Sheridan, R.P.1
  • 13
    • 33646272448 scopus 로고    scopus 로고
    • The minimum significant ratio: A statistical parameter to characterize the reproducibility of potency estimates from concentration-response assays and estimation by replicate-experiment studies
    • Eastwood, B. J.; Farmen, M. W.; Iversen, P. W.; Craft, T. J.; Smallwood, J. K.; Garbison, K. E.; Delapp, N. W.; Smith, G. F. The minimum significant ratio: A statistical parameter to characterize the reproducibility of potency estimates from concentration-response assays and estimation by replicate-experiment studies J. Biomol. Screening 2006, 11, 253-261
    • (2006) J. Biomol. Screening , vol.11 , pp. 253-261
    • Eastwood, B.J.1    Farmen, M.W.2    Iversen, P.W.3    Craft, T.J.4    Smallwood, J.K.5    Garbison, K.E.6    Delapp, N.W.7    Smith, G.F.8
  • 15
    • 70350046704 scopus 로고    scopus 로고
    • Kinase inhibitor data modeling and de novo inhibitor design with fragment approaches
    • Vieth, M.; Erickson, J.; Wang, J.; Webster, Y.; Mader, M.; Higgs, R.; Watson, I. Kinase inhibitor data modeling and de novo inhibitor design with fragment approaches J. Med. Chem. 2009, 52, 6456-6466
    • (2009) J. Med. Chem. , vol.52 , pp. 6456-6466
    • Vieth, M.1    Erickson, J.2    Wang, J.3    Webster, Y.4    Mader, M.5    Higgs, R.6    Watson, I.7
  • 17
    • 0002714543 scopus 로고    scopus 로고
    • Making large-scale support vector machine learning practical
    • Scholkopf, B. Burges, C. J. C. Smola, A. J. MIT Press: Cambridge, MA
    • Joachims, T. Making large-scale support vector machine learning practical. In Advances in Kernel Methods; Scholkopf, B.; Burges, C. J. C.; Smola, A. J., Eds.; MIT Press: Cambridge, MA, 1999; pp 169-184.
    • (1999) Advances in Kernel Methods , pp. 169-184
    • Joachims, T.1
  • 19
    • 84944178665 scopus 로고
    • Hierarchical grouping to optimize an objective function
    • Ward, J. H., Jr. Hierarchical grouping to optimize an objective function J. Am. Stat. Assoc. 1963, 58, 236-244
    • (1963) J. Am. Stat. Assoc. , vol.58 , pp. 236-244
    • Ward Jr., J.H.1
  • 20
    • 84863304598 scopus 로고    scopus 로고
    • R Development Core Team; R Foundation for Statistical Computing: Vienna, Austria
    • R Development Core Team R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2013.
    • (2013) R: A Language and Environment for Statistical Computing
  • 22
    • 84884278542 scopus 로고    scopus 로고
    • TIBCO: Somerville, MA
    • Spotfire, 4.0; TIBCO: Somerville, MA, 2012.
    • (2012) Spotfire, 4.0
  • 24
    • 42949088496 scopus 로고    scopus 로고
    • Chemical fragments as foundations for understanding target space and activity prediction
    • Sutherland, J. J.; Higgs, R. E.; Watson, I.; Vieth, M. Chemical fragments as foundations for understanding target space and activity prediction J. Med. Chem. 2008, 51, 2689-2700
    • (2008) J. Med. Chem. , vol.51 , pp. 2689-2700
    • Sutherland, J.J.1    Higgs, R.E.2    Watson, I.3    Vieth, M.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.