PLS-optimal: A stepwise D-Optimal design based on latent variables

Journal of Chemical Information and Modeling

Volumn 52, Issue 4, 2012, Pages 975-983

  Brandmaier, Stefan ^a,b   Sahlin, Ullrika ^a   Tetko, Igor V ^b,c   Öberg, Tomas ^a  

^a LINNAEUS UNIVERSITY   (Sweden)

^b INSTITUTE OF EPIDEMIOLOGY   (Germany)

^c EADMET GmbH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

LEAST SQUARES APPROXIMATIONS; OPTIMAL SYSTEMS; RISK ASSESSMENT;

EXPERIMENTAL DESIGN TECHNIQUES; FINANCIAL RESOURCES; PARTIAL LEAST SQUARES MODELS; PRINCIPAL COMPONENTS; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; ROOT-MEAN-SQUARE ERRORS; SEQUENTIAL APPROACH; STEPWISE PROCEDURE;

DESIGN OF EXPERIMENTS;

ALGORITHM; ANIMAL; ARTICLE; CHEMICAL DATABASE; CHEMISTRY; CYPRINIFORMES; DRUG DESIGN; DRUG EFFECT; GROWTH, DEVELOPMENT AND AGING; HIGH THROUGHPUT SCREENING; LD 50; METHODOLOGY; MOLECULAR LIBRARY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REGRESSION ANALYSIS; TETRAHYMENA PYRIFORMIS; DRUG EFFECTS;

ALGORITHMS; ANIMALS; CYPRINIDAE; DATABASES, CHEMICAL; DRUG DESIGN; HIGH-THROUGHPUT SCREENING ASSAYS; LEAST-SQUARES ANALYSIS; LETHAL DOSE 50; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RESEARCH DESIGN; SMALL MOLECULE LIBRARIES; TETRAHYMENA PYRIFORMIS;

MOLECULAR LIBRARY;

EID: 84862021618     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci3000198     Document Type: Article

References (45)

1. Rovida, C.; Hartung, T. Re-evaluation of animal numbers and costs for in vivo tests to accomplish REACH legislation requirements for chemicals - a report by the transatlantic think tank for toxicology (t (4)). ALTEX 2009, 26, 187.208.
2. Eriksson, L.; Jaworska, J.; Worth, A. P.; Cronin, M. T. D.; McDowell, R. M.; Gramatica, P. Methods for Reliability and Uncertainty Assessment and for Applicability Evaluations of Classification-and Regression-Based QSARs. Environ. Health Perspect. 2003, 111.
3. O'berg, T. A QSAR for the hydroxyl radical reaction rate constant: validation, domain of application, and prediction. Atmos. Environ. 2005, 39, 2189-2200.
4. Shoichet, B. K. VVrtual screening of chemical libraries. Nature 2004, 432, 862-865.
5. Taylor, R. Simulation Analysis of Experimental Design Strategies for Screening Random Compounds as Potential New Drugs and Agrochemicals. J. Chem. Inf. Comput. Sci. 1995, 35, 59-67.
6. Stenberg, M.; Linusson, A.; Tysklind, M.; Andersson, P. L. A multivariate chemical map of industrial chemicals. Assessment of various protocols for identification of chemicals of potential concern. Chemosphere 2009, 76, 878-884.
7. Lahl, U.; Gundert-Remy, U. The use of (Q) SAR Methods in the Context of REACH. Toxicol. Mech. Method. 2008, 18, 149-158. (Pubitemid 351414976)
8. Eichler, U.; Ertl, P.; Gobbi, A.; Rohde, B. Definition of an Optimal Subset of Organic Substituents. Interactive Visual Comparison of Various Selection Algorithms. Internet J. Chem. 1999, 2.
9. Daszykowski, M.; Walczak, B.; Massart, D. L. RRpresentative subset selection. Anal. Chim. Acta 2002, 468, 91-103.
10. Mason, J.; Pickett, S. Partition-based selection. Perspect. Drug Discovery Des. 1997, 7/8, 85-114. (Pubitemid 127486238)
11. Lajiness, M. S. DDssimilarity-based compound selection techniques. Perspect. Drug Discovery Des. 1997, 7/8, 65-84.
12. Wootton, R.; Cranfield, R.; Sheppey, G. C.; Goodford, P. J. PPysicochemical-activity relations in practice. 2. Rational selection of benzenoid substituents. J. Med. Chem. 1975, 18, 607-613.
13. Lundstedt, T.; Seifert, E.; Abramo, L.; Thelin, B.; Nyström, Å.; Pettersen, J.; Bergman, R. Experimental design and optimization. Chemometr. Intell. Lab. 1998, 42, 3-40. (Pubitemid 28440359)
14. Chaudhuri, B. B. HHw to choose a representative subset from a set of data in multi-dimensional space. Pattern Recognit. Lett. 1994, 15, 893-899.
15. Hudson, B. D.; Hyde, R. M.; Rahr, E.; Wood, J.; Osman, J. Parameter Based Methods for Compound Selection from Chemical Databases. Quant. Struct.-Act. Relat. 1996, 15, 285-289. (Pubitemid 26283514)
16. Eriksson, L.; Johansson, E. Multivariate design and modeling in QSAR. Chemometr. Intell. Lab. 1996, 34, 1-19. (Pubitemid 26252937)
17. Baroni, M.; Clementi, S.; Cruciani, G.; Kettaneh-Wold, N.; Wold, S. D-Optimal Designs in QSAR. Quant. Struct.-Act. Relat. 1993, 12, 225-231. (Pubitemid 23323749)
18. Wold, S.; Josefson, M.; Gottfries, J.; Linusson, A. The utility of multivariate design in PLS modeling. J. Chemometr. 2004, 18, 156-165.
19. Van den Berg, J.; Curtis, A.; Trampert, J. Optimal nonlinear Bayesian experimental design: an application to amplitude versus offset experiments. Geophys. J. Int. 2003, 155, 411-421. (Pubitemid 37450452)
20. Pronzato, L. One-step ahead adaptive D-optimal design on a finite design space is asymptotically optimal. Metrika 2010, 71, 219-238.
21. Roy, A.; Ghosal, S.; Rosenberger, W. F. CCnvergence properties of sequential Bayesian D-optimal designs. J. Stat. Plan. Infer. 2009, 139, 425-440.
22. Chaloner, K.; Verdinelli, I. Bayesian Experimental Design: A Review. Stat. Sci. 1995, 10, 273-304.
23. Lundstedt, T.; Thelin, B. A multivariate strategy for optimizing a two-step process. Chemometr. Intell. Lab. 1995, 29, 255-261.
24. Schultz, T. W. TTtratox: Tetrahymena pyriformis population growth impairment endpoint. a surrogate for fish lethality. Toxicol. Mech. Methods 1997, 7, 289-309.
25. Russom, C. L.; Bradbury, S. P.; Broderius, S. J.; Hammermeister, D. E.; Drummond, R. A. PPedicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas). Environ. Toxicol. Chem. 1997, 16, 948-967. (Pubitemid 27199705)
26. Meylan, W.; Howard, P. H.; Boethling, R. S. MMlecular topology/fragment contribution method for predicting soil sorption coefficients. Environ. Sci. Technol. 1992, 26, 1560-1567. (Pubitemid 23372972)
27. US EPA. Estimation Programs Interface SuiteTM for MicrosoftR Windows, v 4-10 2011.
28. Tetko, I. V.; Tanchuk, V. Y. AAplication of Associative Neural Networks for Prediction of Lipophilicity in ALOGPS 2-1 Program. J. Chem. Inf. Comput. Sci. 2002, 42, 1136-1145. (Pubitemid 35360297)
29. Hall, L. H.; Kier, L. B. EEectrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information. J. Chem. Inf. Comput. Sci. 1995, 35, 1039-1045.
30. Tetko, I. V.; Sushko, I.; Pandey, A. K.; Zhu, H.; Tropsha, A.; Papa, E.; O.berg, T.; Todeschini, R.; Fourches, D.; Varnek, A. Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection. J. Chem. Inf. Model. 2008, 48, 1733-1746.
31. Varnek, A.; Kireeva, N.; Tetko, I. V.; Baskin, I. I.; Solov-fev, V. P. EEhaustive QSPR Studies of a Large Diverse Set of Ionic Liquids: How Accurately Can We Predict Melting Points? J. Chem. Inf. Model. 2007, 47, 1111-1122. (Pubitemid 46973725)
32. Sushko, I.; Novotarskyi, S.; Körner, R.; Pandey, A. K.; Rupp, M.; Teetz, W.; Brandmaier, S.; Abdelaziz, A.; Prokopenko, V. V.; Tanchuk, V. Y.; Todeschini, R.; Varnek, A.; Marcou, G.; Ertl, P.; Potemkin, V.; Grishina, M.; Gasteiger, J.; Schwab, C.; Baskin, I. I.; Palyulin, V. A.; Radchenko, E. V.; Welsh, W. J.; Kholodovych, V.; Chekmarev, D.; Cherkasov, A.; Aires-de-Sousa, J.; Zhang, Q.-Y.; Bender, A.; Nigsch, F.; Patiny, L.; Williams, A.; Tkachenko, V.; Tetko, I. V. OOline chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information. J. Chem. Inf. Model. 2011, 25, 533-554.
33. Aguiar, P. F.; de; Bourguignon, B.; Khots, M. S.; Massart, D. L.; Phan-Than-Luu, R. D-optimal designs. Chemometr. Intell. Lab. 1995, 30, 199-210.
34. Fedorov, V. Theory of Optimal Experiments; Academic Press: Waltham, MA, 1972.
35. Olsson, I.-M.; Gottfries, J.; Wold, S. D-optimal onion designs in statistical molecular design. Chemometr. Intell. Lab. 2004, 73, 37-46. (Pubitemid 39198946)
36. Wold, S.; Sjöström, M.; Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemometr. Intell. Lab. 2001, 58, 109-130. (Pubitemid 33033283)
37. OECD Principles for the Validation, for Regulatory Purposes, of (Quantitative) Structure-Activity Relationship Models; OECD: Paris, France, 2004; http://www.oecd.org/dataoecd/33/37/37849783.pdf.
38. Valle, S.; Li, W.; Qin, S. J. SSlection of the Number of Principal Components: The Variance of the Reconstruction Error Criterion with a Comparison to Other Methods†. Ind. Eng. Chem. Res. 1999, 38, 4389-4401.
39. Qin, S. J.; Dunia, R. Determining the number of principal components for best reconstruction. J. Process Control 2000, 10, 245-250. (Pubitemid 30556723)
40. Varnek, A.; Fourches, D.; Horvath, D.; Klimchuk, O.; Gaudin, C.; Vayer, P.; Solovev, V.; Hoonakker, F.; Tetko, I. V.; Marcou, G. ISIDA - Platform for Virtual Screening Based on Fragment and Pharmacophoric Descriptors. Curr. Comput.-Aided Drug Des. 2008, 4, 191-198.
41. Thormann, M.; Vidal, D.; Almstetter, M.; Pons, M. Nomen est Omen: Quantitative Prediction of Molecular Properties Directly from IUPAC Name. Open Appl. Inf. J. 2007, 1, 28-32.
42. Sushko, I.; Novotarskyi, S.; Körner, R.; Pandey, A. K.; Kovalishyn, V. V.; Prokopenko, V. V.; Tetko, I. V. AAplicability domain for in silico models to achieve accuracy of experimental measurements. J. Chemometr. 2010, 24, 202-208.
43. Holmes, G.; Donkin, A.; Witten, I. H. II WEKA: a machine learning workbench, Proceedings of the 1994 Second Australian and New Zealand Conference on Intelligent Information Systems;Brisbane, QLD, Australia, November 29. December 2, 1994; IEEE: Washington, DC, pp 357-361.
44. Wehrens, R. Journal of Statistical Software. J. Stat. Softw. 2007, 18, 1-24.
45. R Development Core Team R: A Language and Environment for Statistical Computing; R Project: Vienna, Austria, 2011.