ArticleJournal of molecular modeling2016
Estimation of melting points of large set of persistent organic pollutants utilizing QSPR approach.
Article in Journal of molecular modeling, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed, 17 citations in OpenAlex.
- Application of Machine Learning Methods to Predict the Air Half-Lives of Persistent Organic Pollutants.Molecules (Basel, Switzerland) · 2023Article
- Modeling Physico-Chemical ADMET Endpoints with Multitask Graph Convolutional Networks.Molecules (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The presence of polyhalogenated persistent organic pollutants (POPs), such as Cl/Br-substituted benzenes, biphenyls, diphenyl ethers, and naphthalenes has been identified in all environmental compartments. The exposure to these compounds can pose potential risk not only for ecological systems, but also for human health. Therefore, efficient tools for comprehensive environmental risk assessment for POPs are required. Among the factors vital for environmental transport and fate processes is melting point of a compound. In this study, we estimated the melting points of a large group (1419 compounds) of chloro- and bromo- derivatives of dibenzo-p-dioxins, dibenzofurans, biphenyls, naphthalenes, diphenylethers, and benzenes by utilizing quantitative structure-property relationship (QSPR) techniques. The compounds were classified by applying structure-based clustering methods followed by GA-PLS modeling. In addition, random forest method has been applied to develop more general models. Factors responsible for melting point behavior and predictive ability of each method were discussed.
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Registered trials
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