The electrotopological state and molecular connectivity indices are defined as a system for molecular-structure description, using the term Structure-Information Representation. This system is built on the depiction of a molecule as a network composed of atoms of varying valence electron counts that constitute the valence state, bonded in discrete patterns constituting an electrotopological state. The system is employed in the structure-activity analysis of two sets of ADMET data. Models are created relating hepatotoxicity and human metabolic stability. The validity of these models makes them useful for activity prediction.
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http://dx.doi.org/10.1002/cbdv.200590116 | DOI Listing |
Environ Pollut
January 2025
Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Despite the significant benefits of aquatic passive sampling (low detection limits and time-weighted average concentrations), the use of passive samplers is impeded by uncertainties, particularly concerning the accuracy of sampling rates. This study employed a systematic evaluation approach based on the combination of meta-analysis and quantitative structure-property relationships (QSPR) models to address these issues. A comprehensive meta-analysis based on extensive data from 298 studies on the Polar Organic Chemical Integrative Sampler (POCIS) identified essential configuration parameters, including the receiving phase (type, mass) and the diffusion-limiting membrane (type, thickness, pore size), as key factors influencing uptake kinetic parameters.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
Chemometrics and Molecular Modeling Laboratory, Department of Chemistry and Physics, Kean University, 1000 Morris Avenue, Union, NJ 07083, USA.
Environ Sci Pollut Res Int
July 2024
Hubei Key Laboratory of Petroleum Geochemistry and Environment (Yangtze University), Wuhan, 430100, China.
The insufficient hazard thresholds of specific individual aromatic hydrocarbon compounds (AHCs) with diverse structures limit their ecological risk assessment. Thus, herein, quantitative structure-activity relationship (QSAR) models for estimating the hazard threshold of AHCs were developed based on the hazardous concentration for 5% of species (HC) determined using the optimal species sensitivity distribution models and on the molecular descriptors calculated via the PADEL software and ORCA software. Results revealed that the optimal QSAR model, which involved eight descriptors, namely, Zagreb, GATS2m, VR3_Dzs, AATSC2s, GATS2c, ATSC2i, ω, and V, displayed excellent performance, as reflected by an optimal goodness of fit (R = 0.
View Article and Find Full Text PDFACS Omega
July 2024
Integrated Drug Discovery, Sanofi, 350 Water St., Cambridge, Massachusetts 02141, United States.
To facilitate the triage of hits from small molecule screens, we have used various AI/ML techniques and experimentally observed data sets to build models aimed at predicting colloidal aggregation of small organic molecules in aqueous solution. We have found that Naïve Bayesian and deep neural networks outperform logistic regression, recursive partitioning tree, support vector machine, and random forest techniques by having the lowest balanced error rate (BER) for the test set. Derived predictive classification models consistently and successfully discriminated aggregator molecules from nonaggregator hits.
View Article and Find Full Text PDFChemosphere
July 2024
LAQV-REQUIMTE - Department of Chemistry and Biochemistry - Faculty of Sciences, University of Porto - Rua do Campo Alegre, S/N, 4169-007, Porto, Portugal. Electronic address:
The accurate prediction of standard vaporization enthalpy (ΔH°) for volatile organic compounds (VOCs) is of paramount importance in environmental chemistry, industrial applications and regulatory compliance. To overcome traditional experimental methods for predicting ΔH° of VOCs, machine learning (ML) models enable a high-throughput, cost-effective property estimation. But despite a rising momentum, existing ML algorithms still present limitations in prediction accuracy and broad chemical applications.
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