QSAR Modeling of Large Heterogeneous Sets of Molecules

Abstract In aquatic toxicology, QSAR models are generally designed for chemicals presenting the same mode of toxic action. Their proper use provides good simulation results. Problems arise when the mechanism of toxicity of a chemical is not clearly identified. Indeed, in that case, the inappropriate application of a specific QSAR model can lead to a dramatic error in the toxicity estimation. With the advent of powerful computers and easy access to them, and the introduction of soft modeling and artificial intelligence in SAR and QSAR, radically different models, designed from large non-congeneric sets of chemicals have been proposed. Some of these new QSAR models are reviewed and their originality, advantages, and limitations are stressed.

[1]  C. Auer,et al.  SAR—The U.S. Regulatory Perspective , 1994 .

[2]  Gerrit Schüürmann,et al.  Feed Forward Backpropagation Neural Networks and their Use in Predicting the Acute Toxicity of Chemicals to the Fathead Minnow , 1997 .

[3]  S. Bradbury,et al.  Predicting modes of toxic action from chemical structure: an overview. , 1994, SAR and QSAR in environmental research.

[4]  Lemont B. Kier,et al.  Structure-activity relationship studies on the toxicities of benzene derivatives: II. An analysis of benzene substituent effects on toxicity , 1986 .

[5]  J. Devillers,et al.  Practical applications of quantitative structure-activity relationships (QSAR) in environmental chemistry and toxicology , 1990 .

[6]  T W Schultz,et al.  QSARs for monosubstituted anilines eliciting the polar narcosis mechanism of action. , 1991, The Science of the total environment.

[7]  Herbert S. Rosenkranz,et al.  Multiple Computer‐Automated structure evaluation program study of aquatic toxicity 1: Guppy , 1999 .

[8]  Ş. Niculescu,et al.  Modeling acute toxicity of chemicals to Daphnia magna: A probabilistic neural network approach , 2001, Environmental toxicology and chemistry.

[9]  Jeff Johnson CHEMICAL ACCIDENT PLANS GO ON THE WEB , 1999 .

[10]  J Devillers A General QSAR Model for Predicting the Acute Toxicity of Pesticides to Lepomis Macrochirus , 2001, SAR and QSAR in environmental research.

[11]  James Devillers,et al.  PREDICTION OF TOXICITY OF ORGANOPHOSPHORUS INSECTICIDES AGAINST THE MIDGE, CHIRONOMUS RIPARIUS, VIA A QSAR NEURAL NETWORK MODEL INTEGRATING ENVIRONMENTAL VARIABLES , 2000 .

[12]  F. Burden,et al.  A quantitative structure--activity relationships model for the acute toxicity of substituted benzenes to Tetrahymena pyriformis using Bayesian-regularized neural networks. , 2000, Chemical research in toxicology.

[13]  Gilles Klopman,et al.  The MultiCASE Program II. Baseline Activity Identification Algorithm (BAIA) , 1998, J. Chem. Inf. Comput. Sci..

[14]  J V Nabholz,et al.  U.S. EPA regulatory perspectives on the use of QSAR for new and existing chemical evaluations. , 1995, SAR and QSAR in environmental research.

[15]  Klaus L.E. Kaiser,et al.  Influence of Data Preprocessing and Kernel Selection on Probabilistic Neural Network Modeling of the Acute Toxicity of Chemicals to the Fathead Minnow and Vibrio fischeri Bacteria , 1998 .

[16]  G. Klopman Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules , 1985 .

[17]  J. Hart The use of data estimation methods by regulatory authorities , 1991 .

[18]  Lutgarde M. C. Buydens,et al.  Test Series Selection from Nonlinear Neural Mapping , 1996 .

[19]  Ş. Niculescu,et al.  Using probabilistic neural networks to model the toxicity of chemicals to the fathead minnow (Pimephales promelas): a study based on 865 compounds. , 1999, Chemosphere.

[20]  H. Redkey,et al.  A new approach. , 1967, Rehabilitation record.

[21]  P. Jurs,et al.  Prediction of fathead minnow acute toxicity of organic compounds from molecular structure. , 1999, Chemical research in toxicology.

[22]  Herbert S. Rosenkranz,et al.  Multiple computer‐automated structure evaluation study of aquatic toxicity II. Fathead minnow , 2000 .

[23]  J. Devillers,et al.  A Noncongeneric Model for Predicting Toxicity of Organic Molecules to Vibrio Fischeri , 1999 .

[24]  Herbert S. Rosenkranz,et al.  Expert‐system comparison of structural determinants of chemical toxicity to environmental bacteria , 1994 .

[25]  J Devillers,et al.  The stochastic regression analysis as a tool in ecotoxicological QSAR studies. , 1989, Biomedical and environmental sciences : BES.

[26]  Gerald J. Niemi,et al.  A comparative study of molecular similarity, statistical, and neural methods for predicting toxic modes of action , 1998 .

[27]  S C Basak,et al.  Predicting acute toxicity (LC50) of benzene derivatives using theoretical molecular descriptors: a hierarchical QSAR approach. , 1997, SAR and QSAR in environmental research.

[28]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[29]  J. Devillers,et al.  A General QSAR Model for Predicting the Acute Toxicity of Pesticides to Oncorhynchus mykiss , 2000, SAR and QSAR in environmental research.

[30]  Lemont B. Kier,et al.  Structure‐activity relationship studies on the toxicities of benzene derivatives: I. An additivity model , 1984 .

[31]  Philip D. Wasserman,et al.  Advanced methods in neural computing , 1993, VNR computer library.

[32]  J. Devillers,et al.  Strengths and Weaknesses of the Backpropagation Neural Network in QSAR and QSPR Studies , 1996 .

[33]  H S Rosenkranz,et al.  Applications of the case/multicase SAR method to environmental and public health situations. , 1999, SAR and QSAR in environmental research.

[34]  P N Judson,et al.  Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR. , 1999, SAR and QSAR in environmental research.

[35]  J. Devillers,et al.  A predictive structure-toxicity model with Daphnia magna , 1987 .

[36]  T W Schultz,et al.  Modeling the Toxicity of Chemicals to Tetrahymena pyriformis Using Molecular Fragment Descriptors and Probabilistic Neural Networks , 2000, Archives of environmental contamination and toxicology.

[37]  J. Devillers,et al.  A new approach in ecotoxicological QSAR studies , 1986 .

[38]  K. Kaiser,et al.  Correlations of Vibrio fischeri bacteria test data with bioassay data for other organisms. , 1998, Environmental health perspectives.

[39]  J. Devillers Autocorrelation Descriptors for Modeling (Eco)Toxicological Endpoints , 2000 .

[40]  J Devillers,et al.  A General QSAR Model for Predicting the Toxicity of Organic Chemicals to Luminescent Bacteria (Microtox® test). , 1995, SAR and QSAR in environmental research.

[41]  Michael Freemantle WHAT ARE THE LIMITS OF CHEMISTRY , 1998 .

[42]  John C. Dearden,et al.  A NOTE OF CAUTION TO USERS OF ECOSAR , 1999 .

[43]  H S Rosenkranz,et al.  Development, characterization and application of predictive-toxicology models. , 1999, SAR and QSAR in environmental research.

[44]  C. Russom,et al.  Predicting modes of toxic action from chemical structure: Acute toxicity in the fathead minnow (Pimephales promelas) , 1997 .