The log P descriptor, despite its usefulness, can be difficult to use, especially for researchers lacking skills in physical chemistry. Moreover this classic measure has been determined in numerous ways, which can result in inconsistant estimates of log P values, especially for relatively complex molecules such as fluorescent probes. Novel measures of hydrophilicity/lipophilicity (the Hydrophilic/Lipophilic Index, HLI) and amphiphilicity (hydrophilic/lipophilic indices for the head group and tail, HLIT and HLIHG, respectively) therefore have been devised. We compare these descriptors with measures based on log P, the standard method for quantitative structure activity relationships (QSAR) studies. HLI can be determined using widely available molecular modeling software, coupled with simple arithmetic calculations. It is based on partial atomic charges and is intended to be a stand-alone measure of hydrophilicity/lipophilicity. Given the wide application of log P, however, we investigated the correlation between HLI and log P using a test set of 56 fluorescent probes of widely different physicochemical character. Overall correlation was poor; however, correlation of HLI and log P for probes of narrowly specified charge types, i.e., non-ionic compounds, anions, conjugated cations, or zwitterions, was excellent. Values for probes with additional nonconjugated quaternary cations, however, were less well correlated. The newly devised HLI can be divided into domain-specific descriptors, HLIT and HLIHG in amphiphilic probes. Determinations of amphiphilicity, made independently by the authors using their respective methods, showed excellent agreement. Quantifying amphiphilicity from partial log P values of the head group (head group hydrophilicity; HGH) and tail (amphiphilicity index; AI) has proved useful for understanding fluorescent probe action. The same limitations of log P apply to HGH and AI, however. The novel descriptors, HLIT and HLIHG, offer analogous advantages to those seen with HLI over log P. The high correlation between log P and HLI, and the concordance between the two systems for assessing amphiphilicity, provide a powerful tool for QSAR studies. It is possible now to select a probe with missing fragments, and thus no log P, AI or HGH; and to estimate these important descriptors from parameters derived from HLI.
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http://dx.doi.org/10.3109/10520295.2013.811287 | DOI Listing |
Sci Rep
January 2025
Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, 11461, Riyadh, Saudi Arabia.
Quantitative structure-property relationship (QSPR) modeling has emerged as a pivotal tool in the field of medicinal chemistry and drug design, offering a predictive framework for understanding the correlation between chemical structure and physicochemical properties. Topological indices are mathematical descriptors derived from the molecular graphs that capture structural features and connectivity, playing a crucial role in QSPR analysis by quantitatively relating chemical structures to their physicochemical properties and biological activities. Lung cancer is characterized by its aggressive nature and late-stage diagnosis, often limiting treatment options and significantly impacting patient survival rates.
View Article and Find Full Text PDFMar Pollut Bull
January 2025
School of Resources and Environment, Nanchang University, Key Laboratory of Poyang Lake Environment and Resource Utilization, Ministry of Education, Nanchang 330031, Jiangxi, China.
The widespread use of perfluoro/polyfluoroalkyl compounds (PFACs) makes it inevitable for them to be released into and affect the environment, and the octanol-water partition coefficient (logK) is a key indicator for evaluating the environmental behavior of trace pollutants and their impact on the environment. However, the determination of logK using experimental means is often time-consuming and laborious, or even unattainable. Therefore, the logKow of 20 per/polyfluoroalkyl compounds obtained from the PubChem database was selected as the object of study, and the 41 chemical descriptors required for modeling were obtained by density-functional theory calculations, and it was found that only two molecular descriptors (A, V) were significantly correlated with the logK, with the correlation of the descriptor A being the was the strongest.
View Article and Find Full Text PDFDatabase (Oxford)
January 2025
Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium.
The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data.
View Article and Find Full Text PDFAgonists of insect hormones, namely molting hormone (MH) and juvenile hormone (JH), disrupt the normal growth of insects and can be employed as insecticides that are harmless to vertebrates. In this study, a series of experiments and computational analyses were conducted to rationally design novel insect hormone agonists. Syntheses and quantitative structure-activity relationship (QSAR) analyses of two MH agonist chemotypes, imidazothiadiazoles and tetrahydroquinolines, revealed that the structural factors important for the ligand-receptor interactions are significantly different between these chemotypes.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
SKL-ESPC and BIC-ESAT, College of Environmental Sciences and Engineering, Peking University, Beijing, China. Electronic address:
Minimal study focused on the association between mixed pollutants in atmospheric particulate matter (PM) and their reproductive health risks. Utilizing a novel quantitative structure-activity relationship (QSAR) integrated machine learning algorithms, we evaluated the mixed reproductive health risks associated with phthalates (PAEs) and organophosphates (OPEs) exposure by assessing the affinities of these compounds binding to estrogen receptors (ER) and androgen receptors (AR). The mixed toxicity equivalent factor (TEF) and mixed toxicity equivalent quantity (TEQ) by the QSAR model were all smaller than the sum TEF and TEQ of individual PAEs and OPEs, which may be due to the antagonistic effect of PAEs and OPEs monomers on reproductive toxicity.
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