Comparative analysis of local and consensus quantitative structure-activity relationship approaches for the prediction of bioconcentration factor

References

• Dearden , J. D. 2004 . “ QSAR modeling of bioaccumulation ” . In Predicting Chemical Toxicity and Fate , Edited by: Conin , M.T.D. and Livingstone , D. J. 333 – 356 . Boca Raton : CRC Press .
   Google Scholar
• M. Pavan, A.P. Worth, and T.I. Netzeva, Review of QSAR Models for Bioconcentration, European Commission, Joint Research Centre, Ispra, Italy, EUR 22327EN, 2006.
   Google Scholar
• Piir , G. , Sild , S. , Roncaglioni , A. , Benfenati , E. and Maran , U. 2010 . QSAR model for the prediction of bioconcentration factor using aqueous solubility and descriptors considering various electronic effects . SAR QSAR Environ. Res. , 21 : 711 – 729 .
   PubMed Web of Science ®Google Scholar
• Guha , R. , Dutta , D. , Jurs , P. C. and Chen , T. 2006 . Local lazy regression: Making use of the neighborhood to improve QSAR predictions . J. Chem. Inf. Model. , 46 : 1836 – 1847 .
   PubMed Web of Science ®Google Scholar
• Lei , B. , Xi , L. , Liu , H. and Yao , X. 2009 . Global, local and novel consensus quantitative structure-activity relationship studies of 4-(phenylaminomethylene)isoquinoline-1,3 (2H, 4H)-diones as potent inhibitors of the cyclin-dependent kinase 4 . Anal. Chim. Acta. , 644 : 17 – 24 .
   PubMed Web of Science ®Google Scholar
• Lei , B. , Ma , Y. , Li , J. , Liu , H. , Yao , X. and Gramatica , P. 2010 . Prediction of the adsorption capability onto activated carbon of a large data set of chemicals by local lazy regression method . Atmos. Environ , 44 : 2954 – 2960 .
   Web of Science ®Google Scholar
• Yuan , H. , Wang , Y. and Cheng , Y. 2007 . Local and global quantitative structure-activity relationship modeling and prediction for the baseline toxicity . J. Chem. Inf. Model. , 47 : 159 – 169 .
   PubMed Web of Science ®Google Scholar
• Papa , E. , Kovarich , S. and Gramatica , P. 2011 . On the use of local and global QSPRs for the prediction of physico-chemical properties of polybrominated diphenyl ethers . Mol. Inf. , 30 : 232 – 240 .
   Google Scholar
• Colombo , A. , Benfenati , E. , Karelson , M. and Maran , U. 2008 . The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity . Chemosphere. , 72 : 772 – 780 .
   PubMed Web of Science ®Google Scholar
• Buchwald , F. , Girschick , T. , Seeland , M. and Kramer , S. 2011 . Using local models to improve (Q)SAR predictivity . Mol. Inf. , 30 : 205 – 218 .
   Web of Science ®Google Scholar
• Puzyn , T. , Gajawicz , A. , Rybacka , A. and Haranczyk , M. 2011 . Global versus local QSPR models for persistent organic pollutants: Balancing between predictivity and economy . Struct. Chem. , 22 : 873 – 884 .
   Web of Science ®Google Scholar
• Helgee , E.A. , Carlsson , L. , Boyer , S. and Norinder , U. 2010 . Evaluation of quantitative structure-activity relationship modeling strategies: Local and global models . J. Chem. Inf. Model , 50 : 677 – 689 .
   PubMed Web of Science ®Google Scholar
• Menziani , M.C. , De Benedetti , P.G. and Karelson , M. 1998 . Theoretical descriptors in quantitative structure – Affinity and selectivity relationship study of potent N4-substituted arylpiperazine 5-HT1(Alpha) receptor antagonists . Bioorgan. Med. Chem. , 6 : 535 – 550 .
   PubMed Web of Science ®Google Scholar
• Gramatica , P. , Pilutti , P. and Papa , E. 2004 . Validated QSAR prediction of OH tropospheric degradation of VOCs: Splitting into training-test sets and consensus modeling . J. Chem. Inf. Comp. Sci , 44 : 1794 – 1802 .
   PubMedGoogle Scholar
• Gramatica , P. , Giani , E. and Papa , E. 2007 . Statistical external validation and consensus modeling: A QSAR case study for Koc prediction . J. Mol. Graph. Model. , 25 : 755 – 766 .
   PubMed Web of Science ®Google Scholar
• Li , J. , Lei , B. , Liu , H. , Yao , X. , Liu , M. and Gramatica , P. 2008 . QSAR study of malanyl-CoA decarboxylase inhibitors using GA-MLR and a new strategy of consensus modeling . J. Comput. Chem. , 29 : 2636 – 2647 .
   PubMed Web of Science ®Google Scholar
• Lozano , S. , Halm-Lemeille , M.-P. , Lepailleur , A. , Rault , S. and Bureau , R. 2010 . Consensus QSAR related to global or MOA models: Application to acute toxicity for fish . Mol. Inf. , 29 : 803 – 813 .
   Web of Science ®Google Scholar
• Bhhatarai , B. , Teetz , W. , Liu , T. , Öberg , T. , Jeliazkova , N. , Kochev , N. , Pukalov , O. , Tetko , I.V. , Kovarich , S. , Papa , E. and Gramatica , P. 2011 . CADASTER QSPR models for predictions of melting and boiling points of perfluorinated chemicals . Mol. Inf. , 30 : 189 – 204 .
   PubMed Web of Science ®Google Scholar
• Sushko , I. , Novotarskyi , S. , Korner , R. , Pandey , A.K. , Cherkasov , A. , Lo , J.Z. , Gramatica , P. , Hansen , K. , Schroeter , T. , Muller , K.R. , Xi , L.L. , Liu , H.X. , Yao , X.J. , Oberg , T. , Hormozdiari , F. , Dao , P.H. , Sahinalp , C. , Todeschini , R. , Polishchuk , P. , Artemenko , A. , Kuz’min , V. , Martin , T.M. , Young , D.M. , Fourches , D. , Muratov , E. , Tropsha , A. , Baskin , I. , Horvath , D. , Marcou , G. , Muller , C. , Varnek , A. , Prokopenko , V.V. and Tetko , I.V. 2010 . Applicability domains for classification problems: Benchmarking of distance to models for Ames mutagenicity set . J. Chem. Inf. Model. , 50 : 2094 – 2111 .
   PubMed Web of Science ®Google Scholar
• Zhu , H. , Tropsha , A. , Fourches , D. , Varnek , A. , Papa , E. , Gramatica , P. , Öberg , T. , Dao , P. , Cherkasov , A. and Tetko , I.V. 2008 . Combinatorial QSAR modeling of chemical toxicants tested against Tetrahymena pyriformis . J. Chem. Inf. Model. , 48 : 766 – 784 .
   PubMed Web of Science ®Google Scholar
• Mannhold , R. , Poda , G.I. , Ostermann , C. and Tetko , I.V. 2009 . Calculation of molecular lipophilicity: State-of-the-art and comparison of log P methods on more than 96,000 compounds . J. Pharm. Sci. , 98 : 861 – 893 .
   PubMed Web of Science ®Google Scholar
• Hewitt , M. , Cronin , M.T.D. , Madden , J.C. , Rowe , P.H. , Johnson , C. , Obi , A. and Enoch , S.J. 2007 . Consensus QSAR models: Do the benefits outweigh the complexity? . J. Chem. Inf. Model. , 47 : 1460 – 1468 .
   PubMed Web of Science ®Google Scholar
• Fernández , A. , Lombardo , A. , Rallo , R. , Roncaglioni , A. , Giralt , F. and Benfenati , E. 2012 . Quantitative consensus of bioaccumulation models for integrated testing strategies . Environ. Int. , 45 : 51 – 58 .
   PubMed Web of Science ®Google Scholar
• Zhao , C. , Boriani , E. , Chana , A. , Roncaglioni , A. and Benfenati , E. 2008 . A new hybrid system of QSAR models for predicting bioconcentration factors (BCF) . Chemosphere. , 73 : 1701 – 1707 .
   PubMed Web of Science ®Google Scholar
• Arnot , J.A. and Gobas , F.A.P.C. 2006 . A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms . Environ. Rev. , 14 : 257 – 297 .
   Google Scholar
• CEFIC Long-range Research Initiative (LRI) project EC07, Establishing a bio-concentration factor (BCF) Gold Standard Database, Cefic, Brussles, Belgium, 2007. Available at http://www.cefic-lri.org/lri-toolbox/bcf and http://ambit.sourceforge.net/euras.
   Google Scholar
• Dimitrov , S. , Dimitrova , N. , Parkerton , T. , Comber , M. , Bonnell , M. and Mekenyan , O. 2005 . Base-line model for identifying the bioaccumulation potential of chemicals . SAR QSAR Environ. Res. , 16 : 531 – 554 .
   PubMed Web of Science ®Google Scholar
• JOELib2 2007-03-03, A Java based cheminfomatics (computational chemistry) library, University of Tübingen, Tübingen, Germany, 2007. Software available at http://www.ra.cs.uni-tuebingen.de/software/joelib and http://sourceforge.net/projects/joelib/.
   Google Scholar
• Steinbeck , C. , Hoppe , C. , Kuhn , S. , Floris , M. , Guha , R. and Willighagen , E.L. 2006 . Recent developments of the chemistry development kit (CDK) – An open-source java library for chemo- and bioinformatics . Curr. Pharm. Des. , 17 : 2111 – 2120 .
   Web of Science ®Google Scholar
• Chemistry Development Kit 1.3, 2007; software available at http://cdk.sourceforge.net
   Google Scholar
• I.V. Tetko, J. Gasteiger, R. Todeschini, A. Mauri, D. Livingstone, P. Ertl, V.A. Palyulin, E.V. Radchenko, N.S. Zefirov, A.S. Makarenko, V.Y. Tanchuk, and V.V. Prokopenko, Virtual computational chemistry laboratory – Design and description, J. Comput.-Aided Mol. Des. 19 (2005), pp. 453–463.
   Google Scholar
• VCCLAB, 2005; software available at http://www.vcclab.org
   Google Scholar
• A.R. Katritzky, M. Karelson, and R. Petrukhin, CODESSA PRO. University of Florida, USA. Software available at http://www.codessa-pro.com
   Google Scholar
• JChem 5.5.1. ChemAxon, 2011; software available at http://www.chemaxon.com/jchem
   Google Scholar
• Daylight Fingerprints, Daylight Chemical Information System, Inc., Laguma Niguel (CA), USA. http://www.daylight.com/dayhtml/doc/theory/theory.finger.html
   Google Scholar
• Rogers , D. and Hahn , M. 2010 . Extended-connectivity fingerprints . J. Chem. Inf. Model. , 50 : 742 – 754 .
   PubMed Web of Science ®Google Scholar
• Hall , M. , Frank , E. , Holmes , G. , Pfahringer , B. , Reutemann , P. and Witten , I.H. 2009 . The WEKA data mining software: An update . SIGKDD Explorations , 11 : 10 – 18 .
   Google Scholar
• I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd ed., Morgan Kaufmann, USA 2005.
   Google Scholar
• Lloyd , S. 1982 . Least squares quantization in PCM . IEEE Trans. Inform. Theory , 28 : 129 – 137 .
   Web of Science ®Google Scholar
• Ward , J.H. 1963 . Hierarchical grouping to optimize an objective function . J. Am. Stat. Assoc. , 58 : 236 – 244 .
   Web of Science ®Google Scholar
• Draper , N.R. and Smith , H. 1981 . Applied Regression Analysis , New York : Wiley .
   Google Scholar
• Katritzky , A.R. , Mu , L. , Lobanov , V.S. and Karelson , M. 1996 . Correlation of boiling points with molecular structure. 1. A training set of 298 diverse organics and a test set of 9 simple inorganics . J. Phys. Chem. , 100 : 10400 – 10407 .
   Web of Science ®Google Scholar
• Katritzky , A.R. , Dobchev , D.A. , Slavov , S. and Karelson , M. 2008 . Legitimate utilization of large descriptor pools for QSPR/QSAR models . J. Chem. Inf. Model. , 48 : 2207 – 2213 .
   PubMed Web of Science ®Google Scholar
• M. Karelson, U. Maran, Y. Wang, and A.R. Katritzky, QSPR and QSAR models derived using large molecular descriptor spaces. A review of CODESSA applications. Collect. Czech. Chem. C. 64 (1999), pp. 1551–1571.
   Google Scholar
• Karelson , M. , Sild , S. and Maran , U. 2000 . Non-linear QSAR treatment of genotoxicity . Mol. Simul. , 24 : 229 – 242 .
   Web of Science ®Google Scholar
• Katritzky , A.R. , Tatham , D.B. and Maran , U. 2001 . The correlation of the solubilities of gases and vapors in methanol and ethanol with their molecular structures . J. Chem. Inf. Comput. Sci. , 41 : 358 – 363 .
   PubMedGoogle Scholar
• Kahn , I. , Fara , D. , Karelson , M. , Andersson , P. and Maran , U. 2005 . General QSPR treatment of the soil sorption coefficients of organic pollutants . J. Chem. Inf. Model. , 45 : 95 – 105 .
   Web of Science ®Google Scholar
• Kahn , I. , Sild , S. and Maran , U. 2007 . Modeling the toxicity of chemicals to Tetrahymena pyriformis using heuristic multi-linear regression and heuristic back-propagation neural networks . J. Chem. Inf. Model. , 47 : 2271 – 2279 .
   PubMed Web of Science ®Google Scholar
• Maran , U. and Sild , S. 2003 . QSAR modeling of genotoxicity on non-congeneric sets of organic compounds . Artif. Intell. Rev. , 20 : 13 – 38 .
   Web of Science ®Google Scholar
• Sild , S. , Maran , U. , Lomaka , A. and Karelson , M. 2006 . Open computing grid for molecular science and engineering . J. Chem. Inf. Model. , 46 : 953 – 959 .
   PubMed Web of Science ®Google Scholar
• Schuller , B. , Demuth , B. , Mix , H. , Rasch , K. , Romberg , M. , Sild , S. , Maran , U. , Bała , P. , del Grosso , E. , Casalegno , M. , Piclin , N. , Pintore , M. , Sudholt , W. and Baldridge , K.K. 2008 . “ Chemomentum – UNICORE 6 based infrastructure for complex applications in science and technology ” . In Euro-Par 2007 Workshops: Parallel Processing , Edited by: Bougé , L. , Forsell , M. , Träff , J.L. , Streit , A. , Ziegler , W. , Alexander , M. and Childs , S. 82 – 93 . Heidelberg : Springer-Verlag, Berlin .
   Google Scholar