![[PDF]](/pb-assets/Icons/adobe-pdf-icon-1498649896243.png){kind=icon}
References
| 1. | Anon. (2003). Proposal Concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). COM(2003)644 final. Brussels, Belgium: European Commission. Website http://europa.eu.int/eur-lex/en/com/pdf/2003/com2003_0644en.html (Accessed 20.12.04). Google Scholar |
| 2. | Anon. (2003). Directive 2003/15/EC of the European Parliament and of the Council of 27 February 2003 amending Council Directive 76/768/EEC on the approximation of the laws of the Member States relating to cosmetic products (Text with EEA relevance). Official Journal of the European Union L66, 26–35. Google Scholar |
| 3. | Van der Jagt, K. , Munn, S. , Tørsløv, J. , & de Brujin, J. (2004). Alternative Approaches can Reduce the Use of Test Animals under REACH. Addendum to the Report “Assessment of Additional Testing Needs under REACH. Effects of (Q)SARs, Risk Based Testing and Voluntary Industry Initiatives”. JRC Report EUR 21405 EN, 25 pp. Ispra, Italy: European Commission Joint Research Centre. Website http://ecb.jrc.it (Accessed 16.3.05). Google Scholar |
| 4. | Worth, A.P. , van Leeuwen, C.J. , & Hartung, T. (2004). The prospects for using (Q)SARs in a changing political environment: high expectations and a key role for the Commission's Joint Research Centre. SAR & QSAR in Environmental Research 15, 331–343. Google Scholar | Crossref |
| 5. | Schultz, T.W. , Cronin, M.T.D. , Netzeva, T.I. , & Aptula, A.O. (2002). Structure-toxicity relationships for aliphatic chemicals evaluated with Tetrahymena pyriformis. Chemical Research in Toxicology 15, 1602–1609. Google Scholar | Crossref |
| 6. | Jaworska, J. , Aldenberg, T. , & Nikolova, N. (2005). Review of methods for assessing the applicability domains of SARs and QSARs. Final report to the Joint Research Centre (Contract No. ECVA-CCR. 496575-Z). Part 1: Review of statistical methods for QSAR AD estimation by the training set. Website http://ecb.jrc.it/QSAR/Documents (Accessed 16.3.05). Google Scholar |
| 7. | Gramatica, P. , Pilutti, P. , & Papa, E. (2003). Predicting the NO3 radical tropospheric degradability of organic pollutants by theoretical molecular descriptors. Atmospheric Environment 37, 3115–3124. Google Scholar | Crossref |
| 8. | Eriksson, L. , Jaworska, J. , Worth, A.P. , Cronin, M.T.D. , McDowell, R.M. , & Gramatica, P. (2003). Methods for reliability, uncertainty assessment, and applicability evaluations of classification and regression based QSARs. Environmental Health Perspectives 111, 1361–1375. Google Scholar | Crossref |
| 9. | Tong, W. , Xie, Q. , Hong, H. , Shi, L. , Fang, H. , & Perkins, R. (2004). Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity. Environmental Health Perspectives 112, 1249–1254. Google Scholar |
| 10. | Nikolova, N. , & Jaworska, J. (2003). Approaches to measure chemical similarity: a review. QSAR & Combinatorial Science 22, 1006–1026. Google Scholar | Crossref |
| 11. | Cronin, M.T.D. (2003). Quantitative structure-activity relationships for acute aquatic toxicity: the role of mechanism of toxic action in successful modeling. In Quantitative Structure-Activity Relationship (QSAR) Models of Mutagens and Carcinogens (ed. Benigni, R ), pp. 235–258. Boca Raton, FL, USA: CRC Press Google Scholar | Crossref |
| 12. | Schultz, T.W. , Cronin, M.T.D. , Walker, J.D. , & Aptula, A.O. (2003). Quantitative structure-activity relationships (QSARs) in toxicology: a historical perspective. Journal of Molecular Structure: THEOCHEM 622, 1–22. Google Scholar | Crossref |
| 13. | Bradbury, S.P. , & Lipnick, R.L. (1990). Introduction: structural properties for determining mechanisms of toxic action. Environmental Health Perspectives 87, 181–182. Google Scholar |
| 14. | Schultz, T.W. , Sinks, G.D. , & Cronin, M.T.D. (1997). Identification of mechanisms of toxic action of phenols to Tetrahymena pyriformis from molecular descriptors. In Quantitative Structure-Activity Relationships in Environmental Sciences, Vol. VII, Proceedings of QSAR 96, Elsinore, DK, June 24–28, 1996 (ed. Chen, F. , & Schüürmann, G. ), pp. 329–342. Pensacola, FL, USA: SETAC Press. Google Scholar |
| 15. | Patlewicz, G. , Basketter, D.A. , Smith, C.K. , Hotchkiss, S.A. , & Roberts, D.W. (2001). Skin-sensitization structure-activity relationships for aldehydes. Contact Dermatitis 44, 331–336. Google Scholar | Crossref |
| 16. | Roberts, D.W. , & Patlewicz, G. (2002). Mechanism based structure-activity relationships for skin sensitisation: the carbonyl group domain. SAR & QSAR in Environmental Research 13, 145–152. Google Scholar | Crossref |
| 17. | Patlewicz, G.Y. , Wright, Z.M. , Basketter, D.A. , Pease, C.K. , Lepoittevin, J.P. , & Arnau, E.G. (2002). Structure-activity relationships for selected fragrance allergens. Contact Dermatitis 47, 219–226. Google Scholar | Crossref |
| 18. | Anon. (2000). US patent no. 6 036 349: Method and Apparatus for Validation of Model-based Predictions. Issued March 14, 2000. Washington, DC: USA. Google Scholar |
| 19. | Dimitrov, S.D. , Mekenyan, O.G. , Sinks, G.D. , & Schultz, T.W. (2003). Global modeling of narcotic chemicals: ciliate and fish toxicity. Journal of Molecular Structure: THEOCHEM 622, 63–70. Google Scholar | Crossref |
| 20. | Preparata, F.P. , & Shamos, M.I. (1991). Computational Geometry: An Introduction, 390 pp. New York, NY, USA: Springer Verlag. Google Scholar |
| 21. | Stanton, D.T. , & Jurs, P.C. (1991). Computer-assisted prediction of normal boiling points of furans, tetrahydrofurans, and thiophenes. Journal of Chemical Information and Computer Sciences 31, 301–310. Google Scholar |
| 22. | Stanton, D.T. , Egolf, L.M. , & Jurs, P.C. (1992). Computer-assisted prediction of normal boiling points of pyrans and pyrroles. Journal of Chemical Information and Computer Sciences 32, 306–316. Google Scholar |
| 23. | Stanton, D.T. (2000). Development of a quantitative structure-property relationship model for estimating normal boiling points of small multifunctional organic molecules. Journal of Chemical Information and Computer Sciences 40, 81–90. Google Scholar | Crossref |
| 24. | Seber, G.A.F. (2004). Multivariate Observations, 686 pp. New York, NY, USA: John Wiley & Sons. Google Scholar |
| 25. | Atkinson, A.C. (1991). Plots, Transformation, Regression, 282 pp. Oxford, UK: Clarendon Press. Google Scholar |
| 26. | Gramatica, P. , Pilutti, P. , & Papa, E. (2004). Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modeling. Journal of Chemical Information and Computer Sciences 44, 1794–1802. Google Scholar | Crossref |
| 27. | Tropsha, A. , Gramatica, P. , & Gombar, V. (2003). The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR & Combinatorial Science 2, 69–77. Google Scholar | Crossref |
| 28. | Kulkarni, S.A. , Raje, D.V. , & Chakrabarti, T. (2001). Quantitative structure-activity relationships based on functional and structural characteristics of organic compounds. SAR and QSAR in Environmental Research 12, 565–591. Google Scholar | Crossref |
| 29. | Gramatica, P. (2004). Evaluation of Different Statistical Approaches to the Validation of Quantitative Structure-activity Relationships. Final report to the Joint Research Centre. Contract No. ECVA-CCR. 496576-Z. 177 pp. Website http://ecb.jrc.it/QSAR/Documents (Accessed 16.3.05). Google Scholar |
| 30. | Silverman, B. W. (1986). Density Estimation for Statistics and Data Analysis, 176 pp. London, UK: Chapman & Hall. Google Scholar | Crossref |
| 31. | Gray, A. , & Moore, A. (2003). Nonparametric Density Estimation: Toward Computational Tractability. In Proceedings of SIAM International Conference on Data Mining, San Francisco, USA, 2003, 9 p. Website www.siam.org/meetings.sdm03/ (Accessed 16.3.05). Google Scholar |
| 32. | Gray, A. , & Moore, A. (2003). Very fast multivariate kernel density estimation using via computational geometry. Proceedings of Joint Statistics Meeting 2003. Alexandria, VA, USA: The American Statistical Association (Website http://www.amstat.org/meetings/jsm/2003 (Accessed 16.3.05). Google Scholar |
| 33. | Chen, M-H. , & Shao, Q-M. (1999). Monte Carlo estimation of bayesian credible and HPD intervals. Journal of Computational and Graphical Statistics 8, 69–92. Google Scholar |
| 34. | Tukey, J.W. (1977). Exploratory Data Analysis, 688 pp. Reading, UK: Addison-Wesley. Google Scholar |
| 35. | Debnath, A.K. , Debnath, G. , Shusterman, A.J. , & Hansch, C. (1992). A QSAR investigation of the role of hydrophobicity in regulating mutagenicity in the Ames test. I. Mutagenicity of aromatic and heteroaromatic amines in Salmonella typhimurium TA98 and TA100. Environmental and Molecular Mutagenesis 19, 37–52. Google Scholar | Crossref |
| 36. | Glende, C. , Schmitt, H. , Erdinger, L. , Engelhardt, G. , & Boche, G. (2001). Transformation of mutagenic aromatic amines into non-mutagenic species by alkyl substituents. Part I. Alkylation ortho to the amino function. Mutation Research 498, 19–37. Google Scholar | Crossref |
| 37. | Klopman, G. (1992). MULTICASE: a hierarchical computer automated structure evaluation program. Quantitative Structure-Activity Relationships 11, 176–184. Google Scholar | Crossref |
| 38. | Klopman, G. , & Chakravarti, S.K. (2003). Structure-activity relationship study of a diverse set of estrogen receptor ligands (I) using MultiCASE expert system. Chemosphere 51, 445–459 Google Scholar | Crossref |
| 39. | Klopman, G. , & Chakravarti, S.K. (2003). Screening of high production volume chemicals for estrogen receptor binding affinity (II) by the MultiCASE expert system. Chemosphere 51, 461–468. Google Scholar | Crossref |
| 40. | Jaworska, J. , Aldenberg, T. , & Nikolova, N. (2005). Review of methods for assessing the applicability domains of SARs and QSARs. Final report to the Joint Research Centre (Contract No. ECVA-CCR.496575-Z). Part 2: An approach to determining applicability domain for QSAR group contribution models: an analysis of SRC KOWWIN. Website http://ecb.jrc.it/QSAR/Documents (Accessed 16.3.05). Google Scholar |
| 41. | Meylan, W.M. , & Howard, P.H. (1995). Atom fragment contribution method for estimating octanol water partition-coefficients. Journal of Pharmaceutical Sciences 84, 83–92. Google Scholar | Crossref |
| 42. | Ashby, J. , Tennant, R.W. , Zeiger, E. , & Stasiewicz, S. (1989). Classification according to chemical structure, mutagenicity to Salmonella and level of carcinogenicity of a further 42 chemicals tested for carcinogenicity by the U.S. National Toxicology Program. Mutation Research 223, 73–103. Google Scholar | Crossref |
| 43. | Tennant, R.W. , & Ashby, J. (1991). Classification according to chemical structure, mutagenicity to Salmonella and level of carcinogenicity of a further 39 chemicals tested for carcinogenicity by the U.S. National Toxicology Program. Mutation Research 257, 209–227. Google Scholar | Crossref |
| 44. | Ridings, J.E. , Barratt, M.D. , Cary, R. , Earnshaw, C.G. , Eggington, C.E. , Ellis, M.K. , Judson, P.N. , Langowski, J.J. , Marchant, C.A. , Payne, M.P. , Watson, W.P. , & Yih, T.D. (1996). Computer prediction of possible toxic action from chemical structure: an update on the DEREK system. Toxicology 106, 267–279. Google Scholar | Crossref |
| 45. | Judson, P.N. , Marchant, C.A. , & Vessey, J.D. (2003). Using argumentation for absolute reasoning about the potential toxicity of chemicals. Journal of Chemical Information and Computer Sciences 43, 1364–1370. Google Scholar | Crossref | Medline |
| 46. | Smithing, M.P. , & Darvas, F. (1992). HazardExpert: an expert system for predicting chemical toxicity. In Food Safety Assessment (ed. Finley, J.W. , Robinson, S.F. , & Armstrong, D.J. ), ACS Symposium Series, pp. 191–200. Washington, DC, USA: American Chemical Society. Google Scholar | Crossref |
| 47. | Woo, Y. , Lai, D.Y. , Argus, M.F. , & Arcos, J.C. (1995). Development of structure-activity relationship rules for predicting carcinogenic potential of chemicals. Toxicology Letters 79, 219–228. Google Scholar | Crossref |
| 48. | Gerner, I. , Zinke, S. , Graetschel, G. , & Schlede, E. (2000). Development of a decision support system for the introduction of alternative methods into local irritancy/corrosivity testing strategies. Creation of fundamental rules for a decision support system. ATLA 28, 665–698. Google Scholar |
| 49. | Hong, H. , Tong, W. , Fang, H. , Shi, L. , Xie, Q. , Wu, J. , Perkins, R. , Walker, J.D. , Branham, W. , & Sheehan, D.M. (2002). Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts. Environmental Health Perspectives 110, 29–36. Google Scholar | Crossref |
| 50. | Potts, R.O. , & Guy, R.H. (1992). Predicting skin permeability. Pharmaceutical Research 9, 663–669. Google Scholar | Crossref |
| 51. | Tong, W. , Hong, H. , Fang, H. , Xie, Q. , & Perkins, R. (2003). Decision forest: combining the predictions of multiple independent decision tree models. Journal of Chemical Information and Computer Sciences 43, 525–531. Google Scholar | Crossref |
| 52. | Tong, W. , Hong, H. , Xie, Q. , Xie, L. , Fang, H. , & Perkins, R. (2004). Assessing QSAR limitations: a regulatory perspective. Current Computer Aided Drug Design 1, 65–72. Google Scholar |
