Prediction for biodegradability of chemicals by an empirical flowchart

Hiromatsu, Koichi; Yakabe, Yoshikuni; Katagiri, K.; Nishihara, Tsutomu

doi:10.1016/s0045-6535(00)00056-4

Cited by 19 publications

(16 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 6 shows the distribution of misclassified chemicals on scatter plot shown in Figure 2. It is In recent studies, mono benzene derivatives and acyclic compounds were properly categorized according to their biodegradability by an empirical flowchart [5]. Also, an expert software system called CATABOL was shown to be a high-precision tool to predict the biodegradability of a variety of chemicals [8,9].…”

Section: Resultsmentioning

confidence: 99%

“…Many SAR models for predicting the biodegradability of chemicals have been proposed. These models have been constructed by multiple regressions such as multiple linear regression (MLR) and logistic regression [1][2][3], artificial neural networks [4], an empirical flowchart [5] and some expert systems [6][7][8][9]. In particular, multiple regressions have been commonly used for the SAR modeling.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction for Biodegradability of Chemicals by Kernel Partial Least Squares

廣松¹,

高原²,

西原³

et al. 2009

J. Comput. Aided Chem.

Self Cite

View full text Add to dashboard Cite

We predicted the biodegradability of 554 chemicals by using a nonlinear partial least squares (PLS) method, called kernel PLS (KPLS), and compared the predictive performance of KPLS and that of linear PLS, which is widely used for modeling structure-activity relationships. Moreover, prediction using support vector machine (SVM) was performed to confirm the utility of KPLS. The KPLS models correctly categorized 429 (77.4%), 443 (80.0%) and 454 (81.9%) chemicals out of 554, whereas the PLS models were correct for 419 (75.6%), 434 (78.3%) and 439 (79.2%) in cases of using 6, 50 and 89 descriptors, respectively, based on the chemical structures of chemicals analyzed in this study. By properly tuning the necessary parameters, KPLS showed better predictive performance for the biodegradability of chemicals than PLS and SVM did, which showed 79.6% accuracy with 89 descriptors.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction for Biodegradability of Chemicals by Kernel Partial Least Squares

廣松¹,

高原²,

西原³

et al. 2009

J. Comput. Aided Chem.

Self Cite

View full text Add to dashboard Cite

show abstract

“…For instance, the pesticide aldrin has the following HL values collected: 28, 43-63, 10, 183, 273-365, 21-584, and 20-100 days. Definitely, the class 1 is not the case (rule (i)), and the smallest values (10,20,21, and 28) were removed to retain the greater values only. For the rest (43, 63, 183, 273, 365, 30, 584, 30, and 100 (30 were used twice to replace 21 and 20), its geometric mean (rule (ii)) is 111 days associating aldrin with the class 3.…”

Section: Data Setmentioning

confidence: 99%

“…A number of learning approaches for determination of the relationship between degradability and molecular fragments were applied -classical MLR [11,14,15], PLS [19], rule-based [10,20] as well as other methods including neural networks [12,16,34]; the newer data-mining arsenal is available from specific literature [35].…”

Section: Qsbr Analysismentioning

confidence: 99%

“…Multiple QSBR models have been suggested for the estimation of degradability for some homogeneous series of molecules belonging to specific chemical classes [3][4][5][6][7][8][9]; most of them operate with short and congeneric training sets up to 40 compounds, and may have only a limited application. In a newer model [10], the size of a training set is not stated but the model has been validated by a homologous set of 177 mono benzene derivatives and 168 http://www.sccj.net/publications/JCCJ/acyclic compounds only. Some models have been created with very short data sets of 18 compounds [11] and 36 compounds [12].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Alikhanidi

Takahashi

2004

J. Comput. Chem. Jpn.

View full text Add to dashboard Cite

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.

show abstract

Designing Safer Organocatalysts – What Lessons Can Be Learned When the Rebirth of an Old Research Area Coincides with the Advent of Green Chemistry?

Beadham

Gurbisz

Gathergood

2012

Handbook of Green Chemistry

View full text Add to dashboard Cite

The sections in this article are Introduction A Brief History of Organocatalysis Pre‐1950 s : From Humble Beginnings 1950 s –1960 s 1970 s : Organocatalysis Begins in Earnest 1980 s 1990 s 2000–Present Advantages of Organocatalysts Catalysts from the Chiral Pool “Rules of Thumb” for Small Molecule Biodegradability Applied to Organocatalysts Selecting Simple Guidelines for Biodegradability Process of Catalyst Development Analogy Between Organocatalyst Development and Drug Design Analogs of Nornicotine – an Aldol Catalyst Exemplifying “Natural” Toxicity Pharmaceutically Derived Organocatalysts and the Role of Cocatalysts Criteria to Assess the Environmental Impact of an Organocatalyst Conclusion Summary Acknowledgements

show abstract

Prediction for biodegradability of chemicals by an empirical flowchart

Cited by 19 publications

References 10 publications

Prediction for Biodegradability of Chemicals by Kernel Partial Least Squares

Prediction for Biodegradability of Chemicals by Kernel Partial Least Squares

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

Designing Safer Organocatalysts – What Lessons Can Be Learned When the Rebirth of an Old Research Area Coincides with the Advent of Green Chemistry?

Contact Info

Product

Resources

About