Results 241 to 250 of about 822,180 (287)

Atomic charge calculations for quantitative structure—property relationships

Journal of Computational Chemistry, 1992
AbstractA previously published empirical charge scheme has been adapted for use in studies of quantitative structure‐property relationships. New parameters have been developed to allow the inclusion of nitrates, nitriles, sulfides, thiols, thiophenes, and sulfoxides. No changes have been made to the original scheme, thus preserving all previous results.
Steven L. Dixon, Peter C. Jurs
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Quantitative structure–property relationship of distribution coefficients of organic compounds

SAR and QSAR in Environmental Research, 2020
The n-octanol/buffer solution distribution coefficient (or n-octanol/water partition coefficient) is of critical importance for measuring lipophilicity of drug candidates. After 4885 molecular descriptor generation, 15 molecular descriptors were selected to develop quantitative structure-property relationship (QSPR) models for distribution coefficients
Y. Liu, X. Yu, J. Chen
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Prediction of Ultraviolet Spectral Absorbance Using Quantitative Structure−Property Relationships

Journal of Chemical Information and Computer Sciences, 2002
High performance liquid chromatography (HPLC) with ultraviolet (UV) spectrophotometric detection is a common method for analyzing reaction products in organic chemistry. This procedure would benefit from a computational model for predicting the relative response of organic molecules.
William L, Fitch, Malcolm, McGregor, Alan R, Katritzky, Andre, Lomaka, Ruslan, Petrukhin, Mati, Karelson   +5 more
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Quantitative structure–property relationships for pesticides in biopartitioning micellar chromatography

Journal of Chromatography A, 2006
The retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides was studied by quantitative structure-property relationships (QSPR) method. Heuristic method (HM) and support vector machine (SVM) method were used to build linear and nonlinear models, respectively. Compared the results of these two methods,
Weiping, Ma, Feng, Luan, Haixia, Zhang, Xiaoyun, Zhang, Mancang, Liu, Zhide, Hu, Botao, Fan   +6 more
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Quantitative structure-property relationship modeling of Grätzel solar cell dyes

Journal of Computational Chemistry, 2013
With fossil fuel reserves on the decline, there is increasing focus on the design and development of low-cost organic photovoltaic devices, in particular, dye-sensitized solar cells (DSSCs). The power conversion efficiency (PCE) of a DSSC is heavily influenced by the chemical structure of the dye.
Vishwesh, Venkatraman, Per-Olof, Åstrand, Bjørn Kåre, Alsberg   +2 more
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Quantitative Structure–Property Relationship Approach in Formulation Development: an Overview

AAPS PharmSciTech, 2019
Chemoinformatics is emerging as a new trend to set drug discovery which correlates the relationship between structure and biological functions. The main aim of chemoinformatics refers to analyzing the similarity among molecules, searching the molecules in the structural database, finding potential drug molecule and their property. One of the key fields
Ajit S, Kulkarni, Amit J, Kasabe, Manish S, Bhatia, Neela M, Bhatia, Vinod L, Gaikwad   +4 more
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Applications of quantitative structure—property relationships to pharmaceutics

Chemometrics and Intelligent Laboratory Systems, 1994
Abstract Most correlation analysis studies of drugs are concerned with the correlation of in-vivo or in-vitro biological activity with physicochemical or structural molecular parameters. However, it is also important to be able to predict such properties as solubility, melting and boiling points, adsorption behaviour, stability to degradation ...
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Quantitative structure–property relationships (QSPRs) on direct photolysis of PCDDs

Chemosphere, 2001
By the use of partial least squares (PLS) method and 27 quantum chemical descriptors computed by PM3 Hamiltonian, a statistically significant QSPR were obtained for direct photolysis quantum yields (Y) of selected Polychlorinated dibenzo-p-dioxins (PCDDs). The QSPR can be used for prediction.
J, Chen, X, Quan, K W, Schramm, A, Kettrup, F, Yang   +4 more
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Quantitative structure–property relationships on photodegradation of polybrominated diphenyl ethers

Chemosphere, 2006
By partial least squares (PLS) regression, quantitative structure-property relationship (QSPR) models were developed for photodegradation rates (k(p)) and quantum yields (Phi) of polybrominated diphenyl ethers (PBDEs) in methanol/water (8:2), and photodegradation rates in pure methanol by UV light in the sunlight region, respectively.
Junfeng, Niu, Zhenyao, Shen, Zhifeng, Yang, Xingxing, Long, Gang, Yu   +4 more
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Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives

Combinatorial Chemistry & High Throughput Screening, 2018
Aim and Objective: Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors ...
Neda, Ahmadinejad, Fatemeh, Shafiei, Tahereh Momeni, Isfahani   +2 more
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