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Physicochemical Descriptors (physicochemical + descriptor)

Distribution by Scientific Domains
Chemistry100%

Selected Abstracts

Analysis of water solubility data on the basis of HYBOT descriptors.

MOLECULAR INFORMATICS, Issue 9-10 2003
Part 1.
Abstract This work describes the analysis of water-gas phase partitioning data Lw=Cw/Cg for 559 organic chemicals on the basis of physicochemical descriptors calculated by the HYBOT program package. Physicochemical descriptors combined with indicator variables as well as a new approach combining traditional QSAR and molecular similarity are used to take structural features into account. The H-bond acceptor ability of chemicals (i.e. interaction of acceptor atoms with hydrogen atoms of water) is the main factor that influences the partitioning of vapors into water. The simultaneous consideration of H-bond acceptor and donor factors leads to a description of the solubility of vapors with a correlation coefficient of about 0.92. The influence of steric interactions of solutes (characterized by means of molecular polarizability) with water molecules contributes slightly but significantly from the statistics point of view. The use of a set of indicator variables for hydrocarbons and for molecules containing amino, amido, CX3, ether and nitro groups as well as for molecules with ability to form intramolecular hydrogen bonds improves the correlation and helps to take structural features into account. Furthermore, the application of an approach based on the calculation of additional contributions to solubility by considering ,nearest neighbor chemicals' and their difference in physicochemical parameters gives in many cases good results and could be very useful in the analysis of vast data sets. [source]

Characterization of Mixtures Part 1: Prediction of Infinite-Dilution Activity Coefficients Using Neural Network-Based QSPR Models

MOLECULAR INFORMATICS, Issue 11-12 2008
Subhash Ajmani
Abstract The major problem in building QSAR/QSPR models for mixtures lies in their characterization. It has been shown that it is possible to construct QSPR models for the density of binary liquid mixtures using simple mole fraction weighted physicochemical descriptors. Such parameters are unsatisfactory; however, from the point of view of interpretation of the resultant models. In this paper, an alternative mechanism-based approach to the characterization of mixtures has been investigated. It has been shown that while it is not possible to build significant linear models using these descriptors, it has been possible to construct satisfactory artificial neural network models. The performance of these models and the importance of the individual descriptors are discussed. [source]

Analysis of water solubility data on the basis of HYBOT descriptors.

MOLECULAR INFORMATICS, Issue 9-10 2003
Part 1.
Abstract This work describes the analysis of water-gas phase partitioning data Lw=Cw/Cg for 559 organic chemicals on the basis of physicochemical descriptors calculated by the HYBOT program package. Physicochemical descriptors combined with indicator variables as well as a new approach combining traditional QSAR and molecular similarity are used to take structural features into account. The H-bond acceptor ability of chemicals (i.e. interaction of acceptor atoms with hydrogen atoms of water) is the main factor that influences the partitioning of vapors into water. The simultaneous consideration of H-bond acceptor and donor factors leads to a description of the solubility of vapors with a correlation coefficient of about 0.92. The influence of steric interactions of solutes (characterized by means of molecular polarizability) with water molecules contributes slightly but significantly from the statistics point of view. The use of a set of indicator variables for hydrocarbons and for molecules containing amino, amido, CX3, ether and nitro groups as well as for molecules with ability to form intramolecular hydrogen bonds improves the correlation and helps to take structural features into account. Furthermore, the application of an approach based on the calculation of additional contributions to solubility by considering ,nearest neighbor chemicals' and their difference in physicochemical parameters gives in many cases good results and could be very useful in the analysis of vast data sets. [source]

Predictions of peptides' retention times in reversed-phase liquid chromatography as a new supportive tool to improve protein identification in proteomics

PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 4 2009
Tomasz B, czek Dr.
Abstract One of the initial steps of proteomic analysis is peptide separation. However, little information from RP-HPLC, employed for peptides separation, is utilized in proteomics. Meanwhile, prediction of the retention time for a given peptide, combined with routine MS/MS data analysis, could help to improve the confidence of peptide identifications. Recently, a number of models has been proposed to characterize quantitatively the structure of a peptide and to predict its gradient RP-HPLC retention at given separation conditions. The chromatographic behavior of peptides has usually been related to their amino acid composition. However, different values of retention coefficients of the same amino acid in different peptides at different neighborhoods were commonly observed. Therefore, specific retention coefficients were derived by regression analysis or by artificial neural networks (ANNs) with the use of a set of peptides retention. In the review, various approaches for peptide elution time prediction in RP-HPLC are presented and critically discussed. The contribution of sequence dependent parameters (e.g., amphipathicity or peptide sequence) and peptide physicochemical descriptors (e.g., hydrophobicity or peptide length) that have been shown to affect the peptide retention time in LC are considered and analyzed. The predictive capability of the retention time prediction models based on quantitative structure,retention relationships (QSRRs) are discussed in details. Advantages and limitations of various retention prediction strategies are identified. It is concluded that proper processing of chromatographic data by statistical learning techniques can result in information of direct use for proteomics, which is otherwise wasted. [source]