Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds

References

• Patlak , M . 1996 . A testing deadline for endocrine disrupters . Environ. Sci. Technol. , 30 : 540A – 544A .
   Google Scholar
• Anstead , GM , Carlson , KE and Katzenellenbogen , JA . 1997 . The estradiol pharmacophore: ligand structure-estrogen receptor binding affinity relationships and a model for the receptor binding site . Steroids , 62 : 268 – 303 .
   PubMed Web of Science ®Google Scholar
• Fang , H , Tong , W , Shi , LM , Blair , R , Perkins , R , Branham , W , Hass , BS , Xien , Q , Dial , SL , Moland , CL and Sheehan , DM . 2001 . Structure–activity relationships for a large diverse set of natural, synthetic and environmental estrogens . Chem. Res. Toxicol. , 14 : 280 – 294 .
   PubMed Web of Science ®Google Scholar
• Fang , H , Tong , W , Welsh , WJ and Sheehan , DM . 2003 . QSAR models in receptor-mediated effects: the nuclear receptor superfamily . J. Mol. Struct. (THEOCHEM) , 622 : 113 – 125 .
   Google Scholar
• Waller , CL , Minor , DL and McKinney , JD . 1995 . Using three-dimensional quantitative structure–activity relationships to examine estrogen receptor binding affinities of polychlorinated hydroxybiphenyls . Environ. Health Perspect. , 103 : 702 – 707 .
   PubMed Web of Science ®Google Scholar
• Waller , CL , Oprea , TI , Chae , K , Park , H-K , Korach , KS , Laws , SC , Wiese , TE , Kelce , WR and Gray , LE . 1996 . Ligand-based identification of environmental estrogens . Chem. Res. Toxicol. , 9 : 1240 – 1248 .
   PubMed Web of Science ®Google Scholar
• Wiese , TE , Polin , LA , Palomino , E and Brooks , SC . 1997 . Induction of the estrogen specific mitogenic response of MCF-7 cells by selected analogues of estradiol-17β: A 3D QSAR Study . J. Med. Chem. , 40 : 3659 – 3669 .
   PubMedGoogle Scholar
• Tong , W , Perkins , R , Strelitz , R , Collantes , ER , Keenan , S , Welsh , WJ , Branham , WS and Sheehan , DM . 1997 . Quantitative structure–activity relationships (QSARs) for estrogen binding to the estrogen receptor: predictions across species . Environ. Health Perspect. , 105 : 1116 – 1124 .
   PubMed Web of Science ®Google Scholar
• Tong , W , Lowis , DR , Perkins , R , Chen , Y , Welsh , WJ , Goddette , DW , Heritage , TW and Sheehan , DM . 1998 . Evaluation of quantitative structure–activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor . J. Chem. Inf. Comput. Sci. , 38 : 669 – 677 .
   PubMedGoogle Scholar
• Cramer , RD III , Patterson , DE and Bunce , JD . 1988 . Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier Proteins . J. Am. Chem. Soc. , 110 : 5959 – 5967 .
   PubMed Web of Science ®Google Scholar
• Beger , RD , Freeman , JP , Lay , JO , Wilkes , JG and Miller , DW . 2001 . Use of 13C NMR spectrometric data to produce a predictive model of estrogen receptor binding activity . J. Chem. Inf. Comput. Sci. , 41 : 219 – 224 .
   PubMedGoogle Scholar
• Zheng , W and Tropsha , A . 2000 . Novel variable selection quantitative stucture–property relationship approach based on the k-nearest-neighbor principle . J. Chem. Inf. Comput. Sci. , 40 : 185 – 194 .
   PubMedGoogle Scholar
• Manallack , D , Pitt , W , Gancia , E , Montana , J , Livingstone , D , Ford , M and Whitley , D . 2002 . Selecting screening candidates for kinase and G protein-coupled receptor targets using neural networks . J. Chem. Inf. Comput. Sci. , 42 : 1256 – 1262 .
   Google Scholar
• Devillers , J , Domine , D , Guillon , C and Karcher , W . 1998 . Simulating lipophilicity of organic molecules with a back-propagation neural network . J. Pharm. Sci. , 87 : 1086 – 1090 .
   PubMed Web of Science ®Google Scholar
• Tetko , I . 2002 . Neural networks studies 4. Introduction to associative neural networks . J. Chem. Inf. Comput. Sci. , 42 : 717 – 728 .
   PubMedGoogle Scholar
• Tetko , I and Tanchuk , V . 2002 . Application of associative neural networks for prediction of lipophilicity in ALOGPS 2.1 program . J. Chem. Inf. Comput. Sci. , 42 : 1136 – 1145 .
   PubMedGoogle Scholar
• Sutherland , J and Weaver , D . 2003 . Development of quantitative structure–activity relationships and classification models for anticonvulsant activity of hydantoin analogues . J. Chem. Inf. Comput. Sci. , 43 : 1028 – 1036 .
   PubMedGoogle Scholar
• Mattioni , B , Kauffman , G and Jurs , P . 2003 . Predicting the genotoxicity of secondary and aromatic amines using data subsetting to generate a model ensemble . J. Chem. Inf. Comput. Sci. , 43 : 949 – 963 .
   PubMedGoogle Scholar
• Kastenholtz , M , Pastor , M , Cruciani , G , Haaksma , E and Fox , T . 2000 . GRID/CPCA: a new computational tool to design selective ligands . J. Med. Chem. , 43 : 3033 – 3044 .
   Google Scholar
• Tong , W , Hong , H , Fang , H , Xie , Q and Perkins , R . 2003 . Decision forest: combining the predictions of multiple independent decision tree models . J. Chem. Inf. Comput. Sci. , 43 : 525 – 531 .
   PubMedGoogle Scholar
• Sussman , N , Arena , V , Yu , S , Mazumdar , S and Thampatty , B . 2003 . Decision tree SAR models for developmental toxicity based on an FDA/TERIS database . SAR QSAR Environ. Res. , 14 : 83 – 96 .
   PubMed Web of Science ®Google Scholar
• Dewar , MJS , Zoebisch , EG , Healy , EF and Stewart , JJP . 1985 . AM1: a new general purpose quantum-mechanical molecular model . J. Am. Chem. Soc. , 107 : 3902 – 3909 .
   Web of Science ®Google Scholar
• Todeschini, R., Consonni, V. and Pavan, M. DRAGON. Software for the calculation of molecular descriptors, version 3.0. [www: http://www.telemacus.it/talete/].
   Google Scholar
• Metropolis , N , Rosenbluth , AW , Rosenbluth , MN , Teller , AH and Teller , E . 1953 . Equation of state calculations by fast computing machines . J. Chem. Phys. , 21 : 1087 – 1092 .
   Web of Science ®Google Scholar
• Kirkpatrick , S , Gelatt , CD Jr and Vecchi , MP . 1983 . Optimization by simulated annealing . Science , 220 : 671 – 680 .
   PubMed Web of Science ®Google Scholar
• Minka, T., LIGHTSPEED MATLAB Toolbox [www: http://www.stat.cmu.edu/∼ minka/software/lightspeed/].
   Google Scholar
• Tropsha , A , Gramatica , P and Gombar , V . 2003 . The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models . QSAR Comb. Sci. , 22 : 69 – 76 .
   Google Scholar
• Golbraikh , A and Tropsha , A . 2002 . Beware of q 2! . J. Mol. Graph. Model. , 20 : 269 – 276 .
   PubMed Web of Science ®Google Scholar
• Golbraikh , A and Tropsha , A . 2002 . Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection . J. Comput. Aid. Mol. Des. , 16 : 357 – 369 .
   PubMed Web of Science ®Google Scholar
• Coats , E . 1998 . The CoMFA steroids as a benchmark dataset for development of 3D QSAR methods . Perspect. Drug Discov. Design , 12/13/14 : 199 – 213 .
   Google Scholar
• Tuppurainen , K , Viisas , M , Laatikainen , R and Peräkylä , M . 2002 . Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set . J. Chem. Inf. Comput. Sci. , 42 : 607 – 613 .
   PubMedGoogle Scholar
• Tetko , I , Tanchuk , V and Villa , A . 2001 . Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices . J. Chem. Inf. Comput. Sci. , 41 : 1407 – 1421 .
   PubMedGoogle Scholar
• Tetko , I , Tanchuk , V , Kasheva , T and Villa , A . 2001 . Estimation of aqueous solubility of chemical compounds using E-state indices . J. Chem. Inf. Comput. Sci. , 41 : 1488 – 1493 .
   PubMedGoogle Scholar
• Golbraikh , A and Tropsha , A . 2003 . QSAR modeling using chirality descriptors derived from molecular topology . J. Chem. Inf. Comput. Sci. , 43 : 144 – 154 .
   PubMedGoogle Scholar