A Theoretical Concept To Rank Environmentally Significant Chemicals

Brüggemann, Rainer; Bartel, Hans-Georg

doi:10.1021/ci9800559

Cited by 69 publications

(41 citation statements)

References 30 publications

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“…Brüggemann and co-workers [39][40][41][42][43][44] have proposed their use as an attractive way of handling complex information within the environmental area. Poset models in ranking or prioritizing chemical pollutants have been proposed [45][46][47][48].…”

Section: Posetic Applications In Generalmentioning

confidence: 99%

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Ivanciuc

Klein

2006

IJMS

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Due to their widespread use in bactericides, insecticides, herbicides, and fungicides, chlorophenols represent an important source of soil contaminants. The environmental fate of these chemicals depends on their physico-chemical properties. In the absence of experimental values for these physico-chemical properties, one can use predicted values computed with quantitative structure-property relationships (QSPR). As an alternative to correlations to molecular structure we have studied the super-structure of a reaction network, thereby developing three new QSSPR models (poset-average, clusterexpansion, and splinoid poset) that can be applied to chemical compounds which can be hierarchically ordered into a reaction network. In the present work we illustrate these poset QSSPR models for the correlation of the octanol/water partition coefficient (log K ow ) and the soil sorption coefficient (log K OC ) of chlorophenols. Excellent results are obtained for all QSSPR poset models to yield: log K ow , r = 0.991, s = 0.107, with the cluster-expansion QSSPR; and log K OC , r = 0.938, s = 0.259, with the spline QSSPR. Thus, the poset QSSPR models predict environmentally important properties of chlorophenols.

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Section: Posetic Applications In Generalmentioning

confidence: 99%

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Ivanciuc

Klein

2006

IJMS

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“…Consequently, another binary algebraic relation can be introduced, the quasi-order. However, since only few substances appear to be equivalent in the present study, this topic is not further deepened here (for details, see [21]). It follows that if A B and B C then A C. If no rank can be established between A and B these compounds are denoted as incomparable, i.e.…”

Section: Partial Order Rankingmentioning

confidence: 91%

QSARs for Prioritizing PBT Substances to Promote Pollution Prevention

Carlsen

Walker

2003

QSAR Comb. Sci.

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The selection of Persistent, Bioaccumulative and Toxic (PBT) substances constitutes an important task in the possible regulation of chemicals. Due to the shortage of experimental data, Quantitative Structure Activity Relationship (QSAR) estimates for these endpoints appear as an attractive alternative. In the present study, biodegradation, bioaccumulation and aquatic toxicity estimates were obtained using the BioWin, BCFWin and ECOSAR modules of the EPI suite. Partial order theory was used to rank QSAR-based PBT substances. The proposed approach is suggested as a decision support tool to facilitate subsequent pollution prevention activities by regulated and regulatory communities.

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“…According to Brüggemann and Bartel (1999), in the HDT, two objects x and y, characterized by the descriptors q 1 (x), q 2 (x), . .…”

Section: Ranking By the Hasse Diagram Technique (Hdt)mentioning

confidence: 99%

A Novel Crossing Minimization Ranking Method

Abdullah

Barnawi

2015

Applied Artificial Intelligence

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The ranking problem consists of comparing a collection of observations and deciding which one is "better." An observation can consist of multiple attributes, multiple data types, and different orders of preference. Due to diverse practical applications, the ranking problem has been receiving attention in the domain of machine learning and statistics, some of those applications being webpage ranking, gene ranking, pesticide risk assessment, credit-risk screening, etc. In this article, we will present and discuss a novel and fast clustering-based algorithmic ranking technique and provide necessary theoretical working. The proposed technique utilizes the interrelationships among the observations to perform ranking and is based on the crossing minimization paradigm from the domain of VLSI chip design. Using laboratory ranking results as a reference, we compare the algorithmic ranking of the proposed technique and two traditional ranking techniques: the Hasse Diagram Technique (HDT) and the Hierarchical Clustering (HC) technique. The results demonstrate that our technique generates better rankings compared to the traditional ranking techniques and closely matches the laboratory results that took days of work.

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A Theoretical Concept To Rank Environmentally Significant Chemicals

Cited by 69 publications

References 30 publications

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

QSARs for Prioritizing PBT Substances to Promote Pollution Prevention

A Novel Crossing Minimization Ranking Method

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