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CBS-QB3 calculation of quantum chemical molecular descriptors of isomeric thiadiazoles

Journal of Molecular Graphics and Modelling, 2006
The results of the calculation of several molecular descriptors of isomeric thiadiazoles through the CBS-QB3 model chemistry are presented in this work. The results could be useful in quantitative structure-activity relationship (QSAR) or quantitative structure-property relationship (QSPR) studies of derivatives of the nitrogen-containing analogs of ...
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Application of quantum topological molecular similarity descriptors in QSPR study of the O‐methylation of substituted phenols

Journal of Computational Chemistry, 2007
AbstractThe usefulness of a novel type of electronic descriptors called quantum topological molecular similarity (QTMS) indices for describing the quantitative effects of molecular electronic environments on the O‐methylation kinetic of substituted phenols has been investigated. QTMS theory produces for each molecule a matrix of descriptors, containing
Bahram Hemmateenejad, Afshan Mohajeri
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QSPRs for Molecular Diffusion Coefficients in Polymeric Passive Samplers: A Comparison of Simple Molecular and Quantum‐mechanical Sigma‐moment Descriptors

Molecular Informatics, 2019
AbstractLinear quantitative structure‐property relationships (QSPRs) for the prediction of diffusion coefficients (log Dp) were developed for organic contaminants in two common passive sampler materials, polydimethylsiloxane (PDMS) and low‐density polyethylene (LDPE).
Alina M. Lampic   +2 more
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A refined model for prediction of hydrogen bond acidity and basicity parameters from quantum chemical molecular descriptors

Physical Chemistry Chemical Physics, 2009
Abraham's H-bonding parameters alpha and beta have been described in terms of a minimal set of readily obtainable molecular descriptors. These parameters are basically equilibrium constants for complexation of acids with a reference base (alpha) or bases with a reference acid (beta) measured in a non-hydrogen bonding solvent such as tetrachloromethane.
Devereux, Mike   +2 more
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New Quantum Mechanics-Based Three-Dimensional Molecular Descriptors for Use in QSSR Approaches:  Application to Asymmetric Catalysis

Journal of Chemical Information and Modeling, 2007
This paper presents a new protocol based on 3D molecular descriptors using QM calculations for use in CoMFA-like 3D-QSSR. The new method was developed and then applied to predict catalytic selectivity in the asymmetric alkylation of aldehydes catalyzed by Zn-aminoalcohols.
Manuel Urbano-Cuadrado   +3 more
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Effects of chemical structures of omega-6 fatty acids on the molecular parameters and quantum chemical descriptors

Journal of Molecular Structure, 2018
Abstract Omega-6 fatty acids are essential polyunsaturated fatty acids (PUFAs) and found in refined vegetable oils, nuts and seed that play a very important role in heart and brain function, along with normal growth and development. These acids have at least two double bonds and 18 carbon atoms (18:2) that the number of double bonds present refers to
Gocen, Tuğba   +2 more
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Descriptors and Probability Distributions in MO Theory: Weighted Mulliken Matrices and Molecular Quantum Similarity Measures

Journal of Mathematical Chemistry, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Multi‐way Analysis of Quantum Topological Molecular Similarity Descriptors for Modeling Acidity Constant of Some Phenolic Compounds

Chemical Biology & Drug Design, 2007
Three‐way analyses of quantum topological molecular similarity descriptors were used for quantitative structure property relationship modeling of the acidity constant of some phenol derivatives. A three‐way data was built for different molecules by constructing a data matrix for each molecule.
Mahnaz, Esteki   +3 more
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Enhancing Molecular Machine Learning with Quantum-Chemical Descriptors and Hybrid Modeling

International Journal of Advanced Nano Computing and Analytics
The integration of quantum chemistry with Machine Learning (ML) offers transformative potential for molecular property prediction, critical for drug design and catalyst development. However, challenges like high computational costs and unreliable predictions in safety-critical applications necessitate robust frameworks.
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