Results 171 to 180 of about 7,878 (217)

SMILES in QSPR/QSAR Modeling: Results and Perspectives

Current Drug Discovery Technologies, 2007
The technique of constructing optimal descriptors calculated with the Simplified molecular input line entry system (SMILES) is described. SMILES based optimal descriptors and descriptors calculated with molecular graphs (hydrogen filled graphs and graph of atomic orbitals) are compared in modeling done by means of quantitative structure - property ...
Andrey A, Toropov, Emilio, Benfenati
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Building a Quantitative Structure-Property Relationship (QSPR) Model

2019
Knowing the physicochemical and general biochemical properties of a compound is critical to understanding how it behaves in different biological environments and to anticipating what is likely to happen in situations where that behavior cannot be measured directly.
Robert D, Clark, Pankaj R, Daga
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Descriptors in Nano-QSAR/QSPR Modeling

2019
The quantitative structure–activity/property relationships (QSAR/QSPR) models are one of the efficient methods supporting the experimental investigations of chemicals, including nanomaterials and their risk assessment, as well as their application potential. This chapter focuses on the computational aspects of characterization of nanostructures in nano-
Ewelina Wyrzykowska, Karolina Jagiello, Bakhtiyor Rasulev, Tomasz Puzyn   +3 more
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QSPR models for physicochemical properties of polychlorinated diphenyl ethers

Science of The Total Environment, 2003
Partial least squares regression together with 17 theoretical molecular structural descriptors was successfully used to develop QSPR models on sub-cooled liquid vapor pressures (P(L)), n-octanol/water partition coefficients (K(OW)) and sub-cooled liquid water solubilities (S(W,L)) of polychlorinated diphenyl ethers (PCDEs).
Ping, Yang, Jingwen, Chen, Shuo, Chen, Xing, Yuan, K-W, Schramm, A, Kettrup   +5 more
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Universal Approach for Structural Interpretation of QSAR/QSPR Models

Molecular Informatics, 2013
AbstractIn this paper we offer a novel approach for the structural interpretation of QSAR models. The major advantage of our developed methodology is its universality, i.e., it can be applied to any QSAR/QSPR model irrespective of chemical descriptors and machine learning methods applied.
Pavel G, Polishchuk, Victor E, Kuz'min, Anatoly G, Artemenko, Eugene N, Muratov   +3 more
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QSAR/QSPR Model Research of Complicated Samples

Advanced Materials Research, 2013
QSAR/QSPR study is a hot issue in present chemical informatics research, and is the very active research domain. In present, a large number of QSAR/QSPR (quantitative structure-activity/property relationships) models have been widely studied and applied in a lot of different areas.
Yan Kun Li, Xiao Ying Ma
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QSPR modeling of nonionic surfactant cloud points: An update

Journal of Colloid and Interface Science, 2011
Quantitative structure-property relationship (QSPR) models for the cloud points of nonionic surfactants were developed based on CODESSA descriptors. Essentials accounting for a reliable model were considered carefully. Four descriptors were selected by a generic algorithm (GA) method to link the structures of nonionic surfactants to their corresponding
Yueying, Ren, Baowei, Zhao, Qing, Chang, Xiaojun, Yao   +3 more
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QSPR Modeling:  Graph Connectivity Indices versus Line Graph Connectivity Indices

Journal of Chemical Information and Computer Sciences, 2000
Five QSPR models of alkanes were reinvestigated. Properties considered were molecular surface-dependent properties (boiling points and gas chromatographic retention indices) and molecular volume-dependent properties (molar volumes and molar refractions). The vertex- and edge-connectivity indices were used as structural parameters.
Basak, Subhash. C., Nikolić, Sonja, Trinajstić, Nenad, Amić, Dragan, Bešlo, Drago   +4 more
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QSPR modeling bioconcentration factor (BCF) by balance of correlations

European Journal of Medicinal Chemistry, 2009
In many cases, quantitative structure-property/activity relationships (QSPRs/QSARs) are built according to the following scheme: (1) a split of the chemicals into a training and test sets; (2) selection of a model satisfactory for the training set; (3) validation of the model with the test set.
A A, Toropov, A P, Toropova, E, Benfenati   +2 more
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Development of QSPR models in BioPPSy environment

2023
Submission note: A thesis submitted in total fulfilment of the requirements for the degree of Doctor of Philosophy to the Department of Chemistry and Physics, Faculty of Science, Health and Engineering, La Trobe University, Bundoora.
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