Predictive Model for Aerobic Biodegradability Developed From a File of Evaluated Biodegradation Data

Howard, Philip H.; Boethling, Robert S.; Stiteler, William; Meylan, William M.; Hueber, Amy E.; Beauman, Julie A.; Larosche, Michael E.

doi:10.1897/1552-8618(1992)11[593:pmfabd]2.0.co;2

Cited by 56 publications

(91 citation statements)

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“…While years of research in physical property modeling and structure activity relationships has resulted in the ability to predict many chemical properties with acceptable reliability from knowledge only of chemical structure, prediction of biodegradability among other properties still needs improvement [13]. Russom et al [14] reported, for example, that for the BIOWIN package [15], the EU recommends only using a slow biodegradation output as confirmation that a substance is not readily biodegradable and recommends against relying on fast biodegradation outputs.…”

Section: Data Availability For Environmental Fate Assessment Of Chemimentioning

confidence: 99%

“…Additionally, while statistical measures can be undertaken to reduce the risk of chance correlations, their possibility remains. It has been reported, for example, that the significance of some variables may be difficult to rationalize given known factors that influence biodegradation [15]. The inability to rationalize the significance of some variables may suggest that they are the result of chance correlations.…”

Section: Regression Modelsmentioning

confidence: 99%

“…The general concept of utilizing multiple models and concluding that reliable results cannot be obtained given conflicting results has been suggested in previous studies [6,15]. However this concept has more recently been rigorously evaluated.…”

Section: Application Of Model Batteriesmentioning

confidence: 99%

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Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

et al. 2004

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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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Section: Data Availability For Environmental Fate Assessment Of Chemimentioning

confidence: 99%

Section: Regression Modelsmentioning

confidence: 99%

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Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

et al. 2004

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“…Howard and coworkers developed linear and nonlinear models to estimate the probability of aerobic biodegradation of 264 compounds from the BIODEG database [13,14]. To classify the compounds to be rapidly or slowly biodegradable, they used 35 fragmental descriptors achieving accuracies for the training set of 90.5 and 89.8% and for the test set (27 chemicals) of 81.5 and 88.8% for linear and nonlinear models, respectively.…”

Section: Introductionmentioning

confidence: 99%

Pesticide Persistence in the Environment - Collected Data and Structure-Based Analysis

Alikhanidi

Takahashi

2004

J. Comput. Chem. Jpn.

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A data set of 420 pesticide persistences in the environment was collected as field half-life (HL) using several on-line databases. Due to the fuzziness of observed values, the compounds were grouped into three major categories: class 1 when a pesticide has HL 30 days; class 2, if 30 < HL 100 days; and class 3, if HL > 100 days. The Quantitative Structure-Biodegradation Relationship (QSBR) analysis was worked out on the training set of 315 pesticides. Thirty one topological substructural descriptors were used and the decision tree approach was employed for the modeling. Estimation results were as follows: for the train set, 5 compounds of two-unity (class 1/class 3) misclassification, 38 compounds of unity (class 1/class 2 and class 2/class 3) misclassification, and 272 compounds (86.3%) were correctly classified; for the test set, there were 3, 20, and 82 compounds (78.1%) respectively. The computer expert system EKeeper was developed on the basis of the QSBR model.

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“…Unfortunately, only a limited number of chemicals (e.g., pesticides and detergents) have received this type of testing. We have reviewed biodegradation data on well over 800 chemicals in the process of developing structure/biodegradation correlations [7]; only approximately 500 chemicals had more than one test result and considerably fewer had consistent results in more than one test. Most chemi-P.H.…”

mentioning

confidence: 99%

Progress report: Determining “real world” biodegradation rates

Howard

1993

Enviro Toxic and Chemistry

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Predictive Model for Aerobic Biodegradability Developed From a File of Evaluated Biodegradation Data

Cited by 56 publications

References 0 publications

Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

Evaluation of Artificial Intelligence Based Models for Chemical Biodegradability Prediction

Pesticide Persistence in the Environment - Collected Data and Structure-Based Analysis

Progress report: Determining “real world” biodegradation rates

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