Two sets of molecular descriptors, the five experimental Abraham, and the five COSMOments of Klamt's COSMO-RS, have been compared for a data set of 470 compounds. Both sets are considered as almost complete sets of LFER. The two sets of descriptors are shown to exhibit a large overlap as far as their chemical content. The chemical information however is distributed differently in each set with the Abraham set incorporating extra information in the excess molar refraction descriptor E. Regression equations have been constructed to predict the experimental Abraham descriptors from theoretically calculated COSMOments. The chemical interpretation of these equations is however difficult because of the lack of clustering which characterizes the distribution of chemical information through the two sets of descriptors. The predictability of the regression equations is tested successfully using a reasonably large set of data, and the method is compared to recent attempts to calculate the Abraham descriptors from various theoretical bases.
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Department of Otolaryngology-Head and Neck Surgery, Icahn School of Medicine at Mount Sinai, New York, New York.
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View Article and Find Full Text PDFArthritis Rheumatol
January 2025
Division of Medicine, Department of Inflammation and Rare Diseases, UCL Centre for Rheumatology, University College London, London, NW3 2PF, UK.
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Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA.
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View Article and Find Full Text PDFEnviron Sci (Camb)
December 2024
Department of Computer Science, Virginia Tech Blacksburg VA 24061 USA
The persistence of pharmaceuticals and personal care products (PPCPs) through wastewater treatment and resulting contamination of aquatic environments and drinking water is a pervasive concern, necessitating means of identifying effective treatment strategies for PPCP removal. In this study, we employed machine learning (ML) models to classify 149 PPCPs based on their chemical properties and predict their removal wastewater and water reuse treatment trains. We evaluated two distinct clustering approaches: C1 (clustering based on the most efficient individual treatment process) and C2 (clustering based on the removal pattern of PPCPs across treatments).
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