Literature DB >> 18007499

Evaluation of artificial intelligence based models for chemical biodegradability prediction.

James R Baker1, Dragan Gamberger, James R Mihelcic, Aleksandar Sabljić.   

Abstract

This study presents a review of biodegradability modeling efforts including a detailed assessment of two models developed using an artificial intelligence based methodology. Validation results for these models using an independent, quality reviewed database, demonstrate that the models perform well when compared to another commonly used biodegradability model, against the same data. The ability of models induced by an artificial intelligence methodology to accommodate complex interactions in detailed systems, and the demonstrated reliability of the approach evaluated by this study, indicate that the methodology may have application in broadening the scope of biodegradability models. Given adequate data for biodegradability of chemicals under environmental conditions, this may allow for the development of future models that include such things as surface interface impacts on biodegradability for example.

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Year:  2004        PMID: 18007499      PMCID: PMC6147355          DOI: 10.3390/91200989

Source DB:  PubMed          Journal:  Molecules        ISSN: 1420-3049            Impact factor:   4.411


  11 in total

1.  Evaluation and application of models for the prediction of ready biodegradability in the MITI-I test.

Authors:  E Rorije; H Loonen; M Müller; G Klopman; W J Peijnenburg
Journal:  Chemosphere       Date:  1999-03       Impact factor: 7.086

2.  Prediction of biodegradation from the atom-type electrotopological state indices.

Authors:  J Huuskonen
Journal:  Environ Toxicol Chem       Date:  2001-10       Impact factor: 3.742

Review 3.  A review of structure-based biodegradation estimation methods.

Authors:  J W Raymond; T N Rogers; D R Shonnard; A A Kline
Journal:  J Hazard Mater       Date:  2001-06-29       Impact factor: 10.588

4.  Predicting ready biodegradability of premanufacture notice chemicals.

Authors:  Robert S Boethling; David G Lynch; Gary C Thom
Journal:  Environ Toxicol Chem       Date:  2003-04       Impact factor: 3.742

Review 5.  Recent developments in broadly applicable structure-biodegradability relationships.

Authors:  Joanna S Jaworska; Robert S Boethling; Philip H Howard
Journal:  Environ Toxicol Chem       Date:  2003-08       Impact factor: 3.742

Review 6.  An overview of the use of quantitative structure-activity relationships for ranking and prioritizing large chemical inventories for environmental risk assessments.

Authors:  Christine L Russom; Roger L Breton; John D Walker; Steven P Bradbury
Journal:  Environ Toxicol Chem       Date:  2003-08       Impact factor: 3.742

7.  Comparison of two methods for obtaining degradation half-lives.

Authors:  Todd Gouin; Ian Cousins; Don Mackay
Journal:  Chemosphere       Date:  2004-08       Impact factor: 7.086

8.  Using Biowin, Bayes, and batteries to predict ready biodegradability.

Authors:  Robert S Boethling; David G Lynch; Joanna S Jaworska; Jay L Tunkel; Gary C Thom; Simon Webb
Journal:  Environ Toxicol Chem       Date:  2004-04       Impact factor: 3.742

9.  Group contribution method for predicting probability and rate of aerobic biodegradation.

Authors:  R S Boethling; P H Howard; W Meylan; W Stiteler; J Beauman; N Tirado
Journal:  Environ Sci Technol       Date:  1994-03-01       Impact factor: 9.028

Review 10.  Impact of biodegradation test methods on the development and applicability of biodegradation QSARs.

Authors:  C E Cowan; T W Federle; R J Larson; T C Feijtel
Journal:  SAR QSAR Environ Res       Date:  1996       Impact factor: 3.000

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  2 in total

1.  Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review.

Authors:  Laure Mamy; Dominique Patureau; Enrique Barriuso; Carole Bedos; Fabienne Bessac; Xavier Louchart; Fabrice Martin-Laurent; Cecile Miege; Pierre Benoit
Journal:  Crit Rev Environ Sci Technol       Date:  2015-06-18       Impact factor: 12.561

2.  Posttraumatic stress disorder: diagnostic data analysis by data mining methodology.

Authors:  Igor Marinić; Fran Supek; Zrnka Kovacić; Lea Rukavina; Tihana Jendricko; Dragica Kozarić-Kovacić
Journal:  Croat Med J       Date:  2007-04       Impact factor: 1.351

  2 in total

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