QSAR workbench: Automating QSAR modeling to drive compound design

Journal of Computer-Aided Molecular Design

Volumn 27, Issue 4, 2013, Pages 321-336

  Cox, Richard ^a   Green, Darren V S ^b   Luscombe, Christopher N ^b   Malcolm, Noj ^a   Pickett, Stephen D ^b  

^a Accelrys Limited UK   (United Kingdom)

^b GLAXOSMITHKLINE   (United Kingdom)

Author keywords

Pipeline pilot; QSAR; Workflow

Indexed keywords

COMPUTATIONAL CHEMISTRY; MODEL BUILDINGS; MOLECULAR GRAPHICS; PIPELINES; STRUCTURAL DESIGN; WEB SERVICES;

CATEGORICAL DATA; CONTINUOUS DATA; DESCRIPTORS; MODEL SPACES; PIPELINE PILOT; QSAR; QSAR MODEL; QSAR MODELING; WORK-FLOWS; WORKFLOW TOOLS;

DIGITAL STORAGE;

ALGORITHM; ARTICLE; AUTOMATION; CHEMISTRY; CONTROLLED STUDY; DRUG DESIGN; DRUG INDUSTRY; INFORMATION PROCESSING; INTERNET; PARTIAL LEAST SQUARES REGRESSION; PREDICTION; PRIORITY JOURNAL; PUBLICATION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; WORKFLOW;

EID: 84878505838     PISSN: 0920654X     EISSN: 15734951     Source Type: Journal    
DOI: 10.1007/s10822-013-9648-4     Document Type: Article

References (50)

1. Hansch C, Selassie C (2007) In: John BT, David JT (eds) Comprehensive Medicinal Chemistry II. Elsevier, Oxford
2. Tropsha A (2007) In: John BT, David JT (eds) Comprehensive Medicinal Chemistry II. Elsevier, Oxford
3. Nicolotti O, Gillet VJ, Fleming PJ, Green DVS (2002) Multiobjective optimization in quantitative structure-activity relationships: deriving accurate and interpretable QSARs. J Med Chem 45:5069-5080
4. Birchall K, Gillet VJ, Harper G, Pickett SD (2008) Evolving interpretable structure-activity relationships. 1. Reduced graph queries. J Chem Inf Model 48:1543-1557
5. Birchall K, Gillet VJ, Harper G, Pickett SD (2008) Evolving interpretable structure-activity relationship models. 2. Using multiobjective optimization to derive multiple models. J Chem Inf Model 48:1558-1570
6. Tropsha A (2010) Best practices for QSAR model development, validation, and exploitation. Mol Inform 29:476-488
7. Cramer RD (2011) Rethinking 3D-QSAR. J Comput Aided Mol Des 25:197-201
8. Bruce CL, Melville JL, Pickett SD, Hirst JD (2007) Contemporary QSAR classifiers compared. J Chem Inf Model 47:219-227
9. Cartmell J, Enoch S, Krstajic D, Leahy D (2005) Automated QSPR through competitive workflow. J Comput Aided Mol Des 19:821-833
10. Rodgers SL, Davis AM, Tomkinson NP, van de Waterbeemd H (2011) Predictivity of simulated ADME AutoQSAR models over time. Mol Inform 30:256-266
11. Davis AM, Wood DJ (2013) Quantitative structure-activity relationship models that stand the test of time. Mol Pharm 10:1183-1190
12. Stalring J, Carlsson L, Almeida P, Boyer S (2011) AZOrange - high performance open source machine learning for QSAR modeling in a graphical programming environment. J Cheminform 3:28
13. Green DVS, Pickett SD, Keefer CE, Bizon C, Woody N, Chakravorty S (2008) Automated predictive modelling: modeller's utopia or fools' gold? http://www.soci.org/News/Fine-Chemoinformatics-SAR
14. Pipeline Pilot (2011) Accelrys Ltd, San Diego. California
15. Chaudry Q, Piclin N, Cotterill J, Pintore M, Price NR, Chretien JR, Roncaglioni A (2010) Global QSAR models of skin sensitisers for regulatory purposes. Chem Cent J 4:S5
16. Zhao C, Boriani E, Chana A, Roncaglioni A, Benfenati E (2008) A new hybrid QSAR model for predicting bioconcentration factor (BCF). Chemosphere 73:1701-1707
17. Lombardo A, Roncaglioni A, Boriani E, Milan C, Benfenati E (2010) Assessment and validation of the CAESAR predictive model for bioconcentration factor (BCF) in fish. Chem Cent J 4:S1
18. Benfenati E (2010) The CAESAR project for in silico models for the REACH legislation. Chem Cent J 4:I1
19. Helium in Excel: a new paradigm for data insight. http://www.prweb.com/ releases/2011/04/prweb5236694.htm Accessed Jan 2013
20. Shearer C (2002) The CRISP-DM model: the new blueprint for data mining. J Data Warehous 5:13-22
21. Harper G, Pickett SD (2006) Methods for mining HTS data. Drug Discovery Today 11:694-699
22. http://www.sencha.com/
23. http://jquery.com
24. http://www.w3.org/TR/xml/
25. http://www.ietf.org/rfc/rfc4627.txt
26. http://www.w3.org/TR/html401
27. http://en.wikipedia.org/wiki/AJAX
28. http://www.w3.org/TR/soap/
29. http://www.w3.org/TR/ws-arch/
30. Brown CD, Davis HT (2006) Receiver operating characteristics curves and related decision measures: a tutorial. Chemom Intell Lab Syst 80:24-38
31. Bi J, Bennett KP (2003) Proceedings of the 20th International Conference on Machine Learning, AAAI Press
32. http://www.caesar-project.eu/index.php
33. http://www.oecd.org/document/2/0,3746,en-2649-34379-42926338-1-1-1-1,00. html
34. Sheridan RP (2013) Time-split cross-validation as a method for estimating the goodness of prospective prediction. J Chem Inf Model. doi: 10.1021/ci400084k
35. Willett P (2006) Enhancing the effectiveness of ligand-based virtual screening using data fusion. QSAR Comb Sci 25:1143-1152
36. Chirico N, Gramatica P (2011) Real external predictivity of QSAR models: how to evaluate it? Comparison of different validation criteria and proposal of using the concordance correlation coefficient. J Chem Inf Model 51:2320-2335
37. Sahigara F, Mansouri K, Ballabio D, Mauri A, Consonni V, Todeschini R (2012) Comparison of different approaches to define the applicability domain of QSAR models. Molecules 17:4791-4810
38. Sheridan RP (2012) Three useful dimensions for domain applicability in QSAR models using random forest. J Chem Inf Model 52:814-823
39. Gombar VK (2000) Method and apparatus for validation of model-based predictions. US19960687726 [US6036349 A]. U.S.A
40. Brown R., Honeycutt D., Aaron S. Quantifying model errors UKQSAR Spring Meeting 2010 http://www.ukqsar.org/slides/Rob-Brown.pdf
41. Xia X, Maliski EG, Gallant P, Rogers D (2004) Classification of kinase inhibitors using a Bayesian model. J Med Chem 47:4463-4470
42. Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman and Hall, London
43. Venables WN, Ripley BD (2002) Modern applied statistics with S, Springer
44. Dimitriadou E, Hornik K, Leisch F, Meyer D, Weingassel A (2010) Misc functions of the department of statistics (e1071) http://CRAN.R-project.org/ package=e1071
45. Wehrens R, Mevik BH (2013) Pls: partial least squares regression (PLSR) and principal component regression (PCR) http://mevik.net/work/software/pls.html
46. Hall LH, Kier LB (1991) The molecular connectivity chi indexes and kappa shape indexes in structure-property modelling. [2], 367-21. New York, VCH Publishers. Reviews in Computational Chemistry. Lipkowitz, K. B. and Boyd, D. B
47. Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742-754
48. Hall LH, Kier LB (1995) Electrotopological state indices for atom types: a novel combination of electronic, topological and valence state information. J Chem Inf Comput Sci 35:1039-1045
49. Hall LH, Mohney B, Kier LB (1991) The electrotopological state: structure information at the atomic level for molecular graphs. J Chem Inf Comput Sci 31:76-82
50. Hall LH, Kier LB (2000) The e-state as the basis for molecular structure space definition and structure similarity. J Chem Inf Comput Sci 40:784-791