Abstract
Structure-activity relationship (SAR) and quantitative structure-activity relationship (QSAR), collectively referred to as (Q)SARs, play an important role in ecological risk assessment (ERA) of organic chemicals. (Q)SARs can fill the data gap for physical-chemical, environmental behavioral and ecotoxicological parameters of organic compounds; they can decrease experimental expenses and reduce the extent of experimental testing (especially animal testing); they can also be used to assess the uncertainty of the experimental data. With the development for several decades, (Q)SARs in environmental sciences show three features: application orientation, multidisciplinary integration, and intelligence. Progress of (Q)SAR technology for ERA of toxic organic compounds, including endpoint selection and mathematic methods for establishing simple, transparent, easily interpretable and portable (Q)SAR models, is reviewed. The recent development on defining application domains and diagnosing outliers is summarized. Model characterization with respect to goodness-of-fit, stability and predictive power is specially presented. The purpose of the review is to promote the development of (Q)SARs orientated to ERA of organic chemicals.
Article PDF
Similar content being viewed by others
References
Macleod M, Mckone T E, Foster K L, Maddalena R L, Parkerton T F, Mackay D. Applications of contaminant fate and bioaccumulation models in assessing ecological risks of chemicals: A case study for gasoline hydrocarbons. Environ Sci Technol, 2004, 38(23): 6225–6233
U. S. Environmental Protection Agency. Guidelines for ecological risk assessment. In: Risk Assessment Forum. Washington: U. S. Environmental Protection Agency, 1998, 63(93): 26846–26924
Verhaar H J M, Solbe J, Speksnijder J, Van Leeuwen C J, Hermens J L M. Classifying environmental pollutants: Part 3. External validation of the classification system. Chemosphere, 2000, 40(8): 875–883
Enterprise & Industry Directorate General and Environment Directorate General. European Commission, REACH in brief. 2002, September. Available online at: http://ecb.jrc.it/REACH
Linkov I, Ames M R, Crouch E A C, Satterstrom F K. Uncertainty in octanol-water partition coefficient: Implications for risk assessment and remedial costs. Environ Sci Technol, 2005, 39(18): 6917–6922
Tunkel J, Mayo K, Austin C, Hickerson A, Howard P. Practical considerations on the use of predictive models for regulatory purposes. Environ Sci Technol, 2005, 39(7): 2188–2199
Cronin M T D, Walker J D, Jaworska J S, Comber M H I, Watts C D, Worth A P. Use of QSARs in international decision-making frameworks to predict ecologic effects and environmental fate of chemical substances. Environ Health Persp, 2003, 111(10): 1376–1390
Li N Q, Wania F, Lei Y D, Daly G L. A Comprehensive and critical compilation, evaluation, and selection of physical-chemical property data for selected polychlorinated biphenyls. J Phy Chem Ref Data, 2003, 32(4): 1545–1590
Hammett L P. Some relations between reaction rates and equilibrium constants. Chem Rev, 1935, 17(1): 125–136
Hammett L P. The effect of structure upon the reactions of organic compounds. Benzene derivatives. J Am Chem Soc, 1937, 59(1): 96–103
Taft R M. Polar and steric substituent constants for aliphatic and o-benzoate groups from rates of esterification and hydrolysis of esters. J Am Chem Soc, 1952, 74(12): 3120–3128
Kaliszan R. Quantitative structure-retention relationships applied to reversed-phase high-performance liquid chromatography. J Chromatogr A, 1993, 656(1–2): 417–435
Goss K-U, Schwarzenbach R P. Linear free energy relationships used to evaluate equilibrium partitioning of organic compounds. Environ Sci Technol, 2001. 35(7): 1–9
Nguyen T H, Goss K-U, Ball W P. Polyparameter linear free energy relationships for estimating the equilibrium partition of organic compounds between water and the natural organic matter in soils and sediments. Environ Sci Technol, 2005, 39(4): 913–924
Chen J W, Pei J, Quan X, Zhao Y Z, Chen S. Linear free energy relationships on rate constants for dechlorination by zero-valent iron. SAR QSAR Environ Res, 2002, 13(6): 597–606
Yan C L, Chen J W, Huang L P, Ding G H, Huang X Y. Linear free energy relationships on rate constants for the gas-phase reactions of hydroxyl radicals with PAHs and PCDD/Fs. Chemosphere, 2005, 61(10): 1523–1528
Chen J W, Peijnenburg W J G M, Quan X, Chen S, Zhao Y Z, Yang F L. The use of PLS algorithms and quantum chemical parameters derived from PM3 Hamiltonian in QSPR studies on direct photolysis quantum yields of substituted aromatic halides. Chemosphere, 2000, 40(12): 1319–1326
Chen J W, Quan X, Schramm K-W, Kettrup A, Yang F L. Quantitative structure-property relationships (QSPRs) on direct photolysis of PCDDs. Chemosphere, 2001, 45(2): 151–159
Free S M, Wilson J M. A mathematical contribution to structure-activity studies. J Med Chem, 1964, 7(4): 395–399
Cramer R D, Patterson D E, Bunce J D. Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc, 1988, 110(18): 5959–5967
Marshall G R, Cramer III R D. Three-dimensional structure-activity relationships. Trends Pharmacol Sci, 1988, 9(8): 285–289
Hansch C, Maloney P P, Fujita T, Muir R M. Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature, 1962, 194(4824): 178–180
Hansch C, Muir R M, Fujita T, Maloney P P, Geiger F, Streich M. The correlation of biological activity of plant growth regulators and chloromycetin derivatives with Hammett constants and partition coefficients. J Am Chem Soc, 1963, 85(18): 2817–2824
Fujita T, Iwasa J, Hansch C. A new substituent constant, π, derived from partition coefficients. J Am Chem Soc, 1964, 86(23): 5175–5180
Hansch C, Clayton J M. Lipophilic character and biological activity of drugs II: the parabolic case. J Pharm Sci, 1973, 62(1): 1–21
Hermens J, Leeueanch P, Musch A. Quantitative structure-activity relationships and mixture toxicity studies of chloro-and alkylanilines at an acute lethal toxicity level to the guppy (Poecilia reticulata). Ecotox Environ Safe, 1984, 8(4): 388–394
Schultz W T, Bryant S E, Lin D T. Structure-toxicity relationships for tetrahymana: aliphatic aldehydes. B Environ Contam Tox, 1994, 52(2): 279–285
Kamlet M J, Abraham M H, Doherty R M, Taft R W. Solubility properties in polymers and biological media. 4. Correlations of octanol/water partition coefficients with solvatochromic parameters. J Am Chem Soc, 1984, 106(2): 464–466
Kamlet M J, Doherty R M, Abboud J-L M, Abraham M H, Taft R W. Solubility: a new look. Chemtech, 1986, 16(9): 566–576
Kamlet M J, Doherty R M, Carr P W, Mackay D, Abraham M H, Taft R W. Linear solvation energy relationships. 44. Parameter estimation rules that allow accurate prediction of octanol/water partition coefficients and other solubility and toxicity properties of polychlorinated biphenyls and polycyclic aromatic hydrocarbons. Environ Sci Technol, 1988, 22(5): 503–509
Leahy D E. Intrinsic molecular volume as a measure of the cavity term in linear solvation energy relationship: octanol-water partition coefficients and aqueous solubilities. J Pharm Sci, 1986, 75(7): 629–639
Abraham M H, Ibrahim A, Zissimos A M. Determination of sets of solute descriptors from chromatographic measurements. J Chromatogr A, 2004, 1037(1–2): 29–47
Wilson L Y, Famini G R. Using theoretical descriptors in quantitative structure-activity relationships: some toxicological indices. J Med Chem, 1991, 34(5): 1668–1674
Famini G R, Renski C A, Wilson L Y. Using theoretical descriptors in quantitative structure-activity relationships: Some physicochemical properties. J Phys Org Chem, 1992, 5(7): 395–408
Reta M, Carr P W, Sadek P C, Rutan S C. Comparative study of hydrocarbon, fluorocarbon, and aromatic bonded RP-HPLC stationary phases by linear solvation energy relationships. Anal Chem, 1999, 71(16): 3484–3496
Kamlet M J, Doherty R M, Veith G D, Taft R W, Abraham M H. Solubility properties in polymers and biological media. 7. An analysis of toxicant properties that influence inhibition of bioluminescence in Photobacterium phosphoreum (the Microtox test). Environ Sci Technol, 1986, 20(7): 690–695
Kamlet M J, Doherty R M, Abraham M H, Veith G D, Abraham D J, Taft R W. Solubility properties in polymers and biological media. 8. An analysis of the factors that influence toxicities of organic nonelectrolytes to the Golden Orfe Fish (Leuciscus idus melanotus). Environ Sci Technol, 1987, 21(2): 149–155
Balakrishnan A, Polli J E. Apical sodium dependent bile acid transporter: a potential prodrug target. Mol Pharmaceutics (Review), 2006, 3(3): 223–230
Webb S R, Durst G L, Pernich D, Hall J. C. Interaction of cyclohexanediones with acetyl coenzyme-a carboxylase and an artificial tar get-site antibody mimic: a Comparative molecular field analysis. J Agric Food Chem, 2000, 48(6): 2506–2511
Yu S J, Keenan S M, Tong W, Welsh W J. Influence of the structural diversity of data sets on the statistical quality of three-dimensional quantitative structure-activity relationship (3D-QSAR) models: Predicting the estrogenic activity of xenoestrogens. Chem Res Toxicol, 2002, 15(10): 1229–1234
Tong W, Lowis D R, Perkins R, Chen Y, Welsh W J, Goddette D W, Heritage T W, Sheehan D M. Evaluation of quantitative structure-activity relationship methods for large-scale prediction of chemicals binding to the estrogen receptor. J Chem Inf Comput Sci, 1998, 38(4): 669–677
Chen J W, Quan X, Zhao Y Z, Yan Y L, Yang F L. Quantitative structure-property relationship studies on n-octanol/water partitioning coefficients of PCDD/Fs. Chemosphere, 2001, 44(6): 1369–1374
Pavan M, Worth A P, Netzeva T I. Review of QSAR Models for Bioconcentration. JRC report EUR EN I-21020. 2006
Chen J W, Harner T, Ding G H, Quan X, Schramm K W, Kettrup A. Universal predictive models on octanol-air partition coefficients at different temperatures for persistent organic pollutants. Environ Toxicol Chem, 2004, 23(10): 2309–2317
Li X H, Chen J W, Zhang L, Qiao X L, Huang L P. The Fragment constant method for predicting octanol-air partition coefficients of persistent organic pollutants at different temperatures. J Phys Chem Ref Data, 2006, 35(3): 1365–1384
Meylan W M, Howard P H, Boethling R S. Molecular topology/fragment contribution method for predicting soil sorption coefficients. Environ Sci Technol, 1992, 26(8): 1560–1567
Schüürmann G, Ebert R-U, Kühne R. Prediction of the sorption of organic compounds into soil organic matter from molecular structure. Environ Sci Technol, 2006, 40(22): 7005–7011
Hermens J L M, Leeuwangh P, Musch A. Quantitative structure-activity relationships and mixture toxicity studies of chloro-and alkylanilines at an acute lethal toxicity level to the guppy (Poecilia reticulata). Ecotoxicol Environ Safe, 1984, 8: 388–394
Bradbury S P, Russom C L, Ankley G T, Schultz T W, Walker J D. Overview of data and conceptual approaches for derivation of quantitative structure-activity relationships for ecotoxicological effects of organic chemicals. Environ Toxicol Chem, 2003, 22(8): 1789–1798
Tong W, Fang H, Hong H, Xie Q, Perkins R, Anson1 J, Sheehan D M. Regulatory application of SAR/QSAR for priority setting of endocrine disruptors: A perspective. Pure Appl Chem, 2003, 75: 2375–2388
Asikainen A, Ruuskanen J, Tuppurainen K. Consensus kNN QSAR: A versatile method for predicting the estrogenic activity of organic compounds in silico. A comparative study with five estrogen receptors and a large, diverse set of ligands. Environ Sci Technol, 2004, 38(24): 6724–6729
Liu H X, Papa E, Gramatica P. QSAR prediction of estrogen activity for a large set of diverse chemicals under the guidance of OECD principles. Chem Res Toxicol, 2006, 19(11): 1540–1548
Raymond J W, Rogers T N, Shonnard D R, Kline A A. A review of structure-based biodegradation estimation methods. J Hazard Mater, 2001, 84(2–3): 189–215
Chen J W, Peijnenburg W J G M, Quan X, Chen S, Martens D, Schramm K W, Kettrup A. Is it possible to develop a QSPR model for direct photolysis half-lives of PAHs under irradiation of sunlight?. Environ Pollut, 2001, 114(1): 137–143
Walker J D. International workshops on QSARs in the environmental sciences—The first 20 years. QSAR Comb Sci, 2003, 22(4): 415–421
Nys G G, Rekker R F. Statistical analysis of a series of partition coefficients with special reference to the predictability of folding of drug molecules. The introduction of hydrophobic fragmental constants (f values). Eur J Med Chem, 1973, 8: 521–535
Taft R W, Lewis I C. The general applicability of a fixed scale of inductive effects. II. Inductive effects of dipolar substituents in the reactivities of m-and p-substituted derivatives of benzene. J Am Chem Soc, 1958, 80(10): 2436–2443
Hansch C, Leo A, Taft R W. A survey of Hammett substituent constants and resonance and field parameters. Chem Rev, 1991, 91(2): 165–195
Hancock C K, Meyers E A, Yager B J. Quantitative separation of hyperconjugation effects from steric substituent constants. J Am Chem Soc, 1961, 83(20): 4211–4213
Charton M. The nature of the ortho effect. II. Composition of the Taft steric parameters. J Am Chem Soc, 1969, 91(3): 615–618
Ghose A K, Crippen G M. Atomic physicochemical parameters for three-dimensional-structure-directed quantitative structure-activity relationships. 2. Modeling dispersive and hydrophobic interactions. J Chem Inf Comput Sci, 1987, 27(1): 21–35
Kamlet M J, Taft R W. The solvatochromic comparison method. I. The beta-scale of solvent hydrogen-bond acceptor (HBA) basicities. J Am Chem Soc, 1976, 98(2): 377–383
Taft R W, Kamlet M J. The solvatochromic comparison method. 2. The alpha-scale of solvent hydrogen-bond donor (HBD) acidities. J Am Chem Soc, 1976, 98(10): 2886–2894
Balaban A T. Using real numbers as vertex invariants for third-generation topological indexes. J Chem Inf Comput Sci, 1992, 32(1): 23–28
Kier L B, Hall L H. The nature of structure-activity relationships and their relation to molecular connectivity. Eur J Med Chem, 1977, 12: 307–312
Karelson M, Lobanov V S, Katritzky A R. Quantum-chemical descriptors in QSAR/QSPR studies. Chem Rev, 1996, 96(3): 1027–1043
Todeschini R, Consonni V. Handbook of Molecular Descriptors. Wiley-VCH: Weinheim, Germany, 2000
Ren R E, Wang H W. Multivariate Statistical Analysis—Theory, Method, Case (in Chinese). Beijing: National Defence Industry Press, 1999
Livingstone D J, Salt D W. Judging the significance of multiple linear regression models. J Med Chem, 2005, 48(3): 661–663
Dudek A Z, Arodz T, Galvez J. Computational methods in developing quantitative structure-activity relationships (QSAR): A review. Comb Chem High T Scr, 2006, 9(3): 213–228
Xu L, Shao X G. Methods of Chemometrics (in Chinese). Beijing: Science Press, 2004
Guha R, Jurs P C. Determining the validity of a QSAR Model-a classification approach. J Chem Inf Model, 2005, 45(1): 65–73
Barnard J M, Downs G M. Clustering of chemical structures on the basis of two-dimensional similarity measures. J Chem Inf Comput Sci, 1992, 32(6): 644–649
Wang H W. Partial Least-Squares Regression-Method and Applications (in Chinese). Beijing: Defense Industry Press, 1999.
Vapnik V. An overview of statistical learning theory. IEEE T Neural Networ, 1999, 10(5): 988–999
Kövesdi I, Dominguez-Rodriguez M F, Ôrfi L, Naray-Szabo G, Varro A, Papp J G, Matyus P. Application of neural networks in structure-activity relationships. Med Res Rev, 1999, 19(3): 249–269
Luan F. Application of support vector machines (SVM) and Radial basis function neural networks (RBFNN) in Chemistry, Environmental Chemistry and Medicinal Chemistry. Doctoral Dissertation (in Chinese). Lanzhou: Lanzhou University, 2006
Yang S, Lu W, Chen N. Support vector regression based QSPR for the prediction of some physicochemical properties of alkyl benzenes. J Mol Struct, 2005, 719(1–3): 119–127
O’Hara-Mays P. Genetic Algorithms in Molecular Modeling. Edited by James Devillers. Principles of QSAR and Drug Design, Vol. 1. New York: Academic Press, Harcourt Brace & Company. 1996. 1–327
Leardi R. Genetic algorithms in chemometrics and chemistry: A review. J Chemometr, 2001, 15(7): 559–569
Liu H X, Zhang R S, Yao X J, Liu M C, Hu Z D, Fan B T. Prediction of the isoelectric point of an amino acid based on GA-PLS and SVMs. J Chem Inf Comput Sci, 2004, 44(1): 161–167
Wanchana S, Yamashita F, Hashida M. QSAR analysis of the inhibition of recombinant CYP 3A4 activity by structurally diverse compounds using a genetic algorithm-combined partial least squares method. Pharm Res, 2003, 20(9): 1401–1408
Liu J J, Cutler G, Li W, Pan Z, Peng S, Hoey T, Chen L, Ling X B. Multiclass cancer classification and biomarker discovery using GA-based algorithms. Bioinformatics, 2005, 21(11): 2691–2697
McInerney M, Dhawan, A P. Use of genetic algorithms with back propagation in training of feed-forward neural networks. In: IEEE International Conference on Neural Networks, 1993. 203–208
Wang H, Yu J. Application study on nonlinear dynamic FIR modeling using hybrid SVM-PLS method. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA) 4, 2004. 3479–3482
Jaworska J S, Comber M, Auer C, Van Leeuwen C J. Summary of a workshop on regulatory acceptance of QSARs for human health and environmental endpoints. Environ Health Persp, 2003, 111(10): 1358–1360
Cronin M T D, Jaworska J S, Walker J D, Comber M H I, Watts C D, Worth A P. Use of QSARs in international decision-making frameworks to predict health effects of chemical substances. Environ Health Persp, 2003, 111(10): 1391–1401
Walker J W L, Carlsen E, Simon-Hettich B. Global government applications of analogues, SARs and QSARs to predict aquatic toxicity, chemical or physical properties, environmental fate parameters and health effects of organic chemicals. SAR QSAR Environ Res, 2002, 13(6): 607–616
Worth A P, Bassan A, De Bruijn J, Saliner A G, Netzeva T, Patlewicz G, Pavan M, Tsakovska I, Eisenreich S. The role of the European Chemicals Bureau in promoting the regulatory use of QSARs methods. SAR QSAR Environ Res, 2007, 18(1—2): 111–125
Organisation for Economic Co-Operation and Development (OECD). Report from the Expert Group on (Q)SARs on the Principles for the Validation of (Q)SARs, 2004. Available online at: http://appli1.oecd.org/olis/2004doc.nsf/linkto/env-jm-mono(2004)24
Organisation for Economic Co-Operation and Development (OECD). Guidance document on the validation of (Quantitative) Structure-Activity Relationships [(Q)SARs] models, 2007. Available online at: http://www.oecd.org/dataoecd/55/22/38131728.pdf
Organisation for Economic Co-Operation and Development (OECD). Testing and assessment Report on the regulatory uses and applications in OECD member countries of (Quantitative) Structure-Activity Relationship[(Q)SARs] models in the assessment of new and existing chemicals, 2006. Available online at: http://appli1.oecd.org/olis/2006doc.nsf/linkto/env-jm-mono(2006)25
Wang L S, Han S K. Quantitative Structure-Activity Relationships of Organic Compounds (in Chinese). Beijing: China Environmental Science Press, 1993
Wang L S. Chemistry of Organic Pollution (in Chinese). Beijing: Higher Education Press, 2004
Chen J W. Quantitative Structure-Property Relationships and Quantitative Structure-Activity Relationships of Organic Pollutants (in Chinese). Dalian: Dalian University of Technology Press, 1999
Ding G H. Application of PLS and GA on QSAR of Selected Organic Pollutants (in Chinese). Doctoral Dissertation. Dalian: Dalian University of Technology, 2006
Lv Q Z, Shen G L, Yu R Q. Genetic training of network using chaos concept: Application to QSAR studies of vibration modes of tetrahedral halides. J Comput Chem, 2002, 23(14): 1357–1365
Zhao C Y. Applications of QSAR in life analytical chemistry and environmental chemistry. Doctoral Dissertation (in Chinese). Lanzhou: Lanzhou University, 2003
Yao Y Y, Xu L, Yang Y Q, Yuan X S. Study on structure-activity relationships of organic compounds: Three new topological indices and their applications. J Chem Inf Comput Sci, 1993, 33(4): 590–594
Lu G H, Yuan X, Zhao Y H. QSAR study on the toxicity of substituted benzenes to the algae (scenedesmus obliquus). Chemsphere, 2001, 44(3): 437–440
Cronin M T D, Schultz T W. Pitfalls in QSAR. J Mol Struct, 2003, 622(1–2): 39–51
Schultz T W, Cronin M T D. Essential and desirable characteristics of ecotoxicity quantitative structure-activity relationships. Environ Toxicol Chem, 2003, 22(3): 599–607
Cronin M T D, Schultz T W. Validation of Vibrio fischeri acute toxicity data: Mechanism of action-based QSARs for nonpolar narcotics and polar narcotic phenols. Sci Total Environ, 1997, 204(1): 75–88
Walker J D, Jaworska J, Comber M H I, Schultz T W, Dearden J C. Guidelines for developing and using quantitative structure-activity relationships. Environ Toxicol Chem, 2003, 22(8): 1653–1665
Livingstone D J. Data Analysis for Chemists: Applications to QSAR and Chemical Product Design. Oxford: Oxford University Press, 1995
Cronin M T D, Schultz T W. Development of quantitative structure-activity relationships for the toxicity of aromatic compounds to Tetrahymena pyriformis: Comparative assessment of methodologies. Chem Res Toxicol, 2001, 14(9):1284–1295
Burden F R, Winkler D A. A quantitative structure-activity relationships model for the acute toxicity of substituted benzenes to Tetrahymena pyriformis using Bayesian-regularized neural networks. Chem Res Toxicol, 2000, 13(6): 436–440
Kholodovych V, Smith J R, Knight D, Abramson S, Kohn J, Welsh W J. Accurate predictions of cellular response using QSPR: A feasibility test of rational design of polymeric biomaterials. Polymer, 2004, 45(22): 7367–7379
Furusjö E, Svenson A, Rahmberg M, Andersson M. The importance of outlier detection and training set slection for reliable environmental QSAR prediction. Chemosphere, 2006, 63(1): 99–108
Dimitrov S, Dimitrova G, Pavlov T, Dimitrova N, Patlewicz G, Niemela J, Mekenyan O. A stepwise approach for defining the applicability domain of SAR and QSAR models. J Chem Inf Model, 2005, 45(4): 839–849
EC (European Commission). Technical Guidance Document on Risk Assessment in support of Commission Directive 93/67/EEC on Risk Assessment for new notified substances and Commission Regulation (EC) No 1488/94 on Risk Assessment for existing substances, and Directive 98/8/EC of the European Parliament and of the Council concerning the placing of bio-cidal products on the market, Parts 3. 2003
Jaworska J S, Nikolova-Jeliazkova N. Aldenberg T. QSAR applicability domain estimation by projection of the training set in descriptor space: A review. Atla-Altern Lab Anim, 2005, 33(5): 445–459
Netzeva T I, Saliner A G, Worth A P. Comparison of the applicability domain of a quantitative structure-activity relationship for estrogenicity with a large chemical inventory. Environ Toxicol Chem, 2006, 25(5): 1223–1230
Sheridan R P, Feuston B P, Maiorov V N, Kearsley S K. Similarity to molecules in the training set is a good discriminator for prediction accuracy in QSAR. J Chem Inf Comput Sci, 2004, 44(6): 1912–1928
Dimitrov S, Koleva Y, Schiltz T W, Walker J D, Mekenyan O. Interspecies quantitative structure-activity relationships model for aldehydes: Aquatic toxicity. Environ Toxicol Chem, 2004, 23(2): 463–470
Schultz T W, Hewitt M, Netzeva T I, Cronin M T D. Assessing applicability domains of toxicological QSARs: Definition, confidence in predicted values, and the role of mechanisms of action. QSAR Comb Sci, 2007, 26(2): 238–254
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 Persp, 2003, 111(10): 1361–1375
Jackson J E. A User’s Guide to Principal Components. New York: John Wiley. 1991
Kolossov E, Stanforth R. The quality of QSAR models: Problems and solutions. SAR QSAR Environ Res, 2007, 18(1–2): 89–100
Tropsha A, Gramatica P, Gombar V K. The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci, 2003, 22(1): 69–77
Livingstone D J, Manallack D T, Tetko I V. Data modeling with neural networks: Advantages and limitations. J Comput Aid Mol Des, 1997, 11(2): 135–142
Hawkins D M. The problem of overfitting. J Chem Inf Comput Sci. 2004, 44(1): 1–12
Zhang P. Model selection via multifold cross validation. Ann Statist, 1993, 21: 299–313
Baumann K, Korff M, Albert H. A systematic evaluation of the benefits and hazards of variable selection in latent variable regression. Part II. Practical applications. J Chemometr, 2002, 16(7): 351–360
Wehrens R, Putter H, Buydens L M C. The bootstrap: A tutorial. Chemom Intell Lab Systems, 2000(1), 54: 35–52
Yasri A, Hartsough D. Toward an optimal procedure for variable selection and QSAR model building. J Chem Inf Comput Sci, 2001, 41(5): 1218–1227
Burden F R, Ford M G, Whitley D C, Winkler D A. Use of automatic relevance determination in QSAR studies using bayesian neural networks. J Chem Inf Comput Sci, 2000, 40(6): 1423–1430
Mitchell T J. An algorithm for the construction of “D-optimal” experimental design. Technometrics, 2000, 42(1): 48–54
Kubinyi H, Hamprecht F A, Mietzner T. Three-dimensional quantitative similarity-activity relationships (3D QSAR) from SEAL similarity matrices. J Med Chem, 1998, 41(14): 2553–2564
Golbraikh A, Tropsha A. Beware of q 2! J Mol Graph Model, 2002, 20(4): 269–276
Schultz T W, Netzeva T I, Cronin M T D. Evaluation of QSARs for ecotoxicity: A method for assigning quality and confidence. SAR QSAR Environ Res, 2004, 15(5–6): 385–397
Deardon J C, Roberts D W. Larger molecules penetrate membranes more readily. J Pharm Pharmacol, 2006, 58: 60
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National Basic Research Program (973) of China (Grant No. 2006CB403302)
Rights and permissions
About this article
Cite this article
Chen, J., Li, X., Yu, H. et al. Progress and perspectives of quantitative structure-activity relationships used for ecological risk assessment of toxic organic compounds. Sci. China Ser. B-Chem. 51, 593–606 (2008). https://doi.org/10.1007/s11426-008-0076-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11426-008-0076-6