The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
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[1] Chunsheng Yin,et al. Structure-activity relationships and response-surface analysis of nitroaromatics toxicity to the yeast (Saccharomyces cerevisiae). , 2002, Chemosphere.
[2] Nikolai S. Zefirov,et al. QSAR for Boiling Points of "Small" Sulfides. Are the "High-Quality Structure-Property-Activity Regressions" the Real High Quality QSAR Models? , 2001, J. Chem. Inf. Comput. Sci..
[3] S C Basak,et al. Prediction of Mutagenicity Utilizing A Hierarchical Qsar Approach , 2001, SAR and QSAR in environmental research.
[4] John C. Dearden,et al. A NOTE OF CAUTION TO USERS OF ECOSAR , 1999 .
[5] M T D Cronin,et al. Quantitative structure-permeability relationships (QSPRs) for percutaneous absorption. , 2002, Toxicology in vitro : an international journal published in association with BIBRA.
[6] John Mandel,et al. The Regression Analysis of Collinear Data. , 1986, Journal of research of the National Bureau of Standards.
[7] Roger E. Critchlow,et al. Beyond mere diversity: tailoring combinatorial libraries for drug discovery. , 1999, Journal of combinatorial chemistry.
[8] J. Stegeman,et al. Cytochrome P450 gene diversity and function in marine animals: past, present, and future , 2000 .
[9] Toby J. Mitchell,et al. An algorithm for the construction of “ D -optimal” experimental designs , 2000 .
[10] L. A. Stone,et al. Computer Aided Design of Experiments , 1969 .
[11] J N Weinstein,et al. Quantitative structure-antitumor activity relationships of camptothecin analogues: cluster analysis and genetic algorithm-based studies. , 2001, Journal of medicinal chemistry.
[12] K. Neve,et al. CoMFA-based prediction of agonist affinities at recombinant wild type versus serine to alanine point mutated D2 dopamine receptors. , 2000, Journal of medicinal chemistry.
[13] F. Burden,et al. Robust QSAR models using Bayesian regularized neural networks. , 1999, Journal of medicinal chemistry.
[14] Han van de Waterbeemd,et al. Chemometric Methods in Molecular Design: van de Waterbeemd/Chemometric , 1995 .
[15] Alexander Golbraikh,et al. Molecular Dataset Diversity Indices and Their Applications to Comparison of Chemical Databases and QSAR Analysis , 2000, J. Chem. Inf. Comput. Sci..
[16] Frank R. Burden,et al. Use of Automatic Relevance Determination in QSAR Studies Using Bayesian Neural Networks , 2000, J. Chem. Inf. Comput. Sci..
[17] R Benigni,et al. Quantitative structure-activity relationships of mutagenic and carcinogenic aromatic amines. , 2000, Chemical reviews.
[18] H Matter,et al. Random or rational design? Evaluation of diverse compound subsets from chemical structure databases. , 1998, Journal of medicinal chemistry.
[19] Eugene A. Coats,et al. The CoMFA Steroids as a Benchmark Dataset for Development of 3D QSAR Methods , 1998 .
[20] Alan J. Miller,et al. A Fedorov Exchange Algorithm for D-optimal Design , 1994 .
[21] D. L. Massart,et al. Optimization in Irregularly Shaped Regions: pH and Solvent Strength in Reversed-Phase High-Performance Liquid Chromatography Separations , 1994 .
[22] Erik Johansson,et al. Multivariate design and modeling in QSAR , 1996 .
[23] S. T. Buckland,et al. An Introduction to the Bootstrap. , 1994 .
[24] R. Cramer,et al. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. , 1988, Journal of the American Chemical Society.
[25] P. Gramatica,et al. Modelling and prediction of soil sorption coefficients of non-ionic organic pesticides by molecular descriptors. , 2000, Chemosphere.
[26] John D. Walker,et al. Structure Activity Relationships For Predicting Ecological Effects Of Chemicals , 2002 .
[27] Robin Taylor,et al. Simulation Analysis of Experimental Design Strategies for Screening Random Compounds as Potential New Drugs and Agrochemicals , 1995, J. Chem. Inf. Comput. Sci..
[28] J. Zupan,et al. Neural Networks in Chemistry , 1993 .
[29] P Willett,et al. Comparison of algorithms for dissimilarity-based compound selection. , 1997, Journal of molecular graphics & modelling.
[30] D. Massart,et al. Application of Nonlinear Regression Functions for the Modeling of Retention in Reversed-Phase LC , 1994 .
[31] Z. Szántó,et al. Comparative three-dimensional quantitative structure-activity relationship study of safeners and herbicides. , 2000, Journal of agricultural and food chemistry.
[32] David Hartsough,et al. Toward an Optimal Procedure for Variable Selection and QSAR Model Building , 2001, J. Chem. Inf. Comput. Sci..
[33] Paola Gramatica,et al. QSAR study on the tropospheric degradation of organic compounds , 1999 .
[34] Svante Wold,et al. Partial least-squares method for spectrofluorimetric analysis of mixtures of humic acid and lignin sulfonate , 1983 .
[35] Alexander Tropsha,et al. Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle , 2000, J. Chem. Inf. Comput. Sci..
[36] Ruth Pachter,et al. Improved QSARs for Predictive Toxicology of Halogenated Hydrocarbons , 2000, Comput. Chem..
[37] A. Tropsha,et al. Beware of q2! , 2002, Journal of molecular graphics & modelling.
[38] S. Weisberg. Plots, transformations, and regression , 1985 .
[39] Roberto Todeschini,et al. A new algorithm for optimal, distance based, experimental design , 1992 .
[40] W. W. Muir,et al. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity , 1980 .
[41] Ettore Novellino,et al. Use of comparative molecular field analysis and cluster analysis in series design , 1995 .
[42] Ulf Norinder,et al. Single and domain mode variable selection in 3D QSAR applications , 1996 .
[43] Peter J. Rousseeuw,et al. Robust regression and outlier detection , 1987 .
[44] S. Wold,et al. Statistical Validation of QSAR Results , 1995 .
[45] Gregory W. Kauffman,et al. QSAR and k-Nearest Neighbor Classification Analysis of Selective Cyclooxygenase-2 Inhibitors Using Topologically-Based Numerical Descriptors , 2001, J. Chem. Inf. Comput. Sci..
[46] H. Kubinyi,et al. Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. , 1998, Journal of medicinal chemistry.
[47] Takahiro Suzuki,et al. Classification of Environmental Estrogens by Physicochemical Properties Using Principal Component Analysis and Hierarchical Cluster Analysis , 2001, J. Chem. Inf. Comput. Sci..
[48] Desire L. Massart,et al. Artificial neural networks in classification of NIR spectral data: Design of the training set , 1996 .
[49] Peter C. Jurs,et al. Development of Quantitative Structure-Activity Relationship and Classification Models for a Set of Carbonic Anhydrase Inhibitors , 2002, J. Chem. Inf. Comput. Sci..
[50] M T D Cronin,et al. The importance of hydrophobicity and electrophilicity descriptors in mechanistically-based QSARs for toxicological endpoints , 2002, SAR and QSAR in environmental research.
[51] Sung Jin Cho,et al. Rational Combinatorial Library Design. 2. Rational Design of Targeted Combinatorial Peptide Libraries Using Chemical Similarity Probe and the Inverse QSAR Approaches , 1998, J. Chem. Inf. Comput. Sci..
[52] T W Schultz,et al. Structure-toxicity relationships for selected halogenated aliphatic chemicals. , 1999, Environmental toxicology and pharmacology.
[53] J Devillers,et al. QSAR Modeling of Large Heterogeneous Sets of Molecules , 2001, SAR and QSAR in environmental research.
[54] Milan Randic,et al. Construction of High-Quality Structure-Property-Activity Regressions: The Boiling Points of Sulfides , 2000, J. Chem. Inf. Comput. Sci..