Assessment and Modeling of the Toxicity of Organic Chemicals to <i>Chlorella vulgaris</i>:  Development of a Novel Database

The quality of quantitative structure-activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol-water partition coefficient (log K ow ). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.

show abstract

“…26 This fact was confirmed once again in the present study by development of a statistically significant, hydrophobicity-dependent model:…”

Section: Resultssupporting

confidence: 80%

“…26,[31][32][33] A strong trend of increasing toxicity with increasing hydrophobicity underpins the data set. Indeed, log K ow alone accounts for approximately 65% of the variance in the toxicity data (eq 2).…”

Section: Discussionmentioning

confidence: 99%

Description of the Electronic Structure of Organic Chemicals Using Semiempirical and ab initio Methods for Development of Toxicological QSARs.

et al. 2005

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previous studies have validated the usefulness of LUMO and LOGP. [33,34] For DL_SVILP compounds were represented by using atom bond descriptions, rings, alkyl and functional groups. [7] Rings include phenyl heterononaromatic-6-membered-ring, hetero-aromatic-6-membered-ring, hetero-nonaromatic-5-membered-ring, hetero-aromatic-5-memberedring, and cyclohexane.…”

Section: Datasetmentioning

confidence: 99%

Multi‐class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods

Lodhi¹,

Muggleton²,

Sternberg³

2010

Molecular Informatics

View full text Add to dashboard Cite

Toxicity prediction is essential for drug design and development of effective therapeutics. In this paper we present an in silico strategy, to identify the mode of action of toxic compounds, that is based on the use of a novel logic based kernel method. The technique uses support vector machines in conjunction with the kernels constructed from first order rules induced by an Inductive Logic Programming system. It constructs multi-class models by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. In order to evaluate the effectiveness of the approach for chemoinformatics problems like predictive toxicology, we apply it to toxicity classification in aquatic systems. The method is used to identify and classify 442 compounds with respect to the mode of action. The experimental results show that the technique successfully classifies toxic compounds and can be useful in assessing environmental risks. Experimental comparison of the performance of the proposed multi-class scheme with the standard multi-class Inductive Logic Programming algorithm and multi-class Support Vector Machine yields statistically significant results and demonstrates the potential power and benefits of the approach in identifying compounds of various toxic mechanisms.

show abstract

“…The algae are primary producers; provide food for higher trophic levels and produce oxygen which is vital for the sustainability of life in aquatic environments (Ma et al, 2007). According to Cronin et al (2004), as compared to the fish and crustaceans, a limited toxicity data are available for these species. However, the experimental determination of toxicities of a large number of chemicals in several test species would be an expensive and time taking approach and the data gap in this domain of ecotoxicology is almost impossible to be filled by the exhaustive testing of each chemical.…”

Section: Introductionmentioning

confidence: 99%

Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches

Basant¹,

Singh

2015

Chemosphere

View full text Add to dashboard Cite

Assessment and Modeling of the Toxicity of Organic Chemicals to Chlorella vulgaris: Development of a Novel Database

Cited by 69 publications

References 35 publications

Description of the Electronic Structure of Organic Chemicals Using Semiempirical and ab initio Methods for Development of Toxicological QSARs.

Description of the Electronic Structure of Organic Chemicals Using Semiempirical and ab initio Methods for Development of Toxicological QSARs.

Multi‐class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods

Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches

Contact Info

Product

Resources

About