Environmental Modelling Based on a Structural Fragments Approach

Lindgren, Fredrik; Nouwen, J.; Loonen, H.E.W.M.; Worth, Andrew; Hansen, Bjørn Henrik; Karcher, W.

doi:10.1159/000463748

Cited by 4 publications

(6 citation statements)

References 10 publications

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“…Eriksson et al [14] demonstrated the use of PLS in combination with biodegradation data. The use of discriminant PLS on binary data has been described earlier by Lindgren et al [15]. The multivariate analyses were performed with SIMCA‐S version 6.0 [16] and the Matlab software package version 4.0 [17].…”

Section: Methodsmentioning

confidence: 99%

“…The power of the variables was equilibrated by scaling them to unit variance for the development of the main model [16]. In the study with fragment interactions, an intermediate type of scaling was applied as described by Lindgren et al [15]. This intermediate scaling (by square root of the standard deviation) equilibrated the power between the structural fragments, but not as far as to unity.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of biodegradability from chemical structure: Modeling of ready biodegradation test data

Loonen¹,

Lindgren²,

Hansen³

et al. 1999

Enviro Toxic and Chemistry

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Biodegradation data were collected and evaluated for 894 substances with widely varying chemical structures. All data were determined according to the Japanese Ministry of International Trade and Industry (MITI) I test protocol. The MITI I test is a screening test for ready biodegradability and has been described by Organization for Economic Cooperation and Development (OECD) test guideline 301 C and European Union (EU) test guideline C4F. The chemicals were characterized by a set of 127 predefined structural fragments. This data set was used to develop a model for the prediction of the biodegradability of chemicals under standardized OECD and EU ready biodegradation test conditions. Partial least squares (PLS) discriminant analysis was used for the model development. The model was evaluated by means of internal cross-validation and repeated external validation. The importance of various structural fragments and fragment interactions was investigated. The most important fragments include the presence of a long alkyl chain; hydroxy, ester, and acid groups (enhancing biodegradation); and the presence of one or more aromatic rings and halogen substituents (retarding biodegradation). More than 85% of the model predictions were correct for using the complete data set. The not readily biodegradable predictions were slightly better than the readily biodegradable predictions (86 vs 84%). The average percentage of correct predictions from four external validation studies was 83%. Model optimization by including fragment interactions improved the model predicting capabilities to 89%. It can be concluded that the PLS model provides predictions of high reliability for a diverse range of chemical structures. The predictions conform to the concept of readily biodegradable (or not readily biodegradable) as defined by OECD and EU test guidelines.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction of biodegradability from chemical structure: Modeling of ready biodegradation test data

Loonen¹,

Lindgren²,

Hansen³

et al. 1999

Enviro Toxic and Chemistry

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the use of expert judgment does not always lead to unequivocal answers; thus, more uniform ways of determining the domain are needed, such as the use of algorithms. Lindgren et al [87] proposed the use of partial least squares (PLS) to accomplish this, while Eriksson et al [106] proposed the use of cluster analysis for establishing that structurally related organic chemicals are represented in each set.…”

Section: General Principles For Developing Qsarsmentioning

confidence: 99%

“…Use of substructures as descriptors is the basis of the group contribution approach and is limited to chemicals which actually contain substructures used in the training set. Examples of group contribution approaches include calculation of log K ow [85], a biodegradation QSAR [86] and use in classification of chemicals [87]. One group contribution method for classifying chemicals is the Substructure‐based Computerized Chemical Selection Expert System (SuCCSES) [15–17,19].…”

Section: General Principles For Developing Qsarsmentioning

confidence: 99%

Guidelines for developing and using quantitative structure‐activity relationships

Walker

Jaworska²,

Comber³

et al. 2003

Enviro Toxic and Chemistry

117

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Abstract-Numerous quantitative structure-activity relationships (QSARs) have been developed to predict properties, fate, and effects of mostly discrete organic chemicals. As the demand for different types of regulatory testing increases and the cost of experimental testing escalates, there is a need to evaluate the use of QSARs and provide some guidance to avoid their misuse, especially as QSARs are being considered for regulatory purposes. This paper provides some guidelines that will promote the proper development and use of QSARs. While this paper uses examples of QSARs to predict toxicity, the proposed guidelines are applicable to QSARs used to predict physical or chemical properties, environmental fate, ecological effects and health effects.

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“…The 3D-QSAR of models of hydantoin-phenylpiperazine derivatives revealed a useful predictive information for the design of new selective ligands for 5-HT1A and α1 receptors [4]. A fast and easy way of chemical characterizing of the compounds has been described [5]. In this case, the QSAR was a regression model that related variation in the biological activity within a homogeneous series of the compounds (y matrix) to their structural properties (x matrix).…”

Section: Introductionmentioning

confidence: 99%

Efficiency of open channel blockers of neuronal nicotinic acetylcholine receptors as estimated from the structure/activity relationship

Kertser

2007

Neurophysiology

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The participation of definite molecular fragments of bis-cationic ammonium compounds in their blocking effect upon neuronal nicotinic acetylcholine receptors (nAChRs) was deduced from the relationship between the blocking efficacy and three-dimensional molecular models of such compounds that have different fragments. The data on the structure and activity of 15 channel blockers were used for this purpose; predicted blocking effects of the substances were calculated. The correlation coefficients between the blocking activity of the compounds and their predicted efficacy were statistically significant (P > 0.95). The results suggest that HCNCCCCCNCH and HCCNCCCCCNCH fragments (atom chains) with the dimensions of 1.42 and 1.36 nm, respectively, provide the most positive contribution, while a HCNCCCCCNCH fragment with the dimension of 1.36 nm corresponds to highly negative contribution to the blocking activity of compounds. Using the data obtained, we identified the optimum compound structures. The mechanism of the blocking effect upon nAChRs is discussed.Keywords: nicotinic acetylcholine receptor, channel blockers, open channel block, bis-quaternary ammonium compounds, quantitative structure/activity relationship (QSAR).

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Environmental Modelling Based on a Structural Fragments Approach

Cited by 4 publications

References 10 publications

Prediction of biodegradability from chemical structure: Modeling of ready biodegradation test data

Prediction of biodegradability from chemical structure: Modeling of ready biodegradation test data

Guidelines for developing and using quantitative structure‐activity relationships

Efficiency of open channel blockers of neuronal nicotinic acetylcholine receptors as estimated from the structure/activity relationship

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