Literature DB >> 7196751

Steric and lipophobic components of the hydrophobic fragmental constant.

B Testa, P Seiler.   

Abstract

Newly calculated increments of molar volumes and surface areas were compared with hydrophobic fragmental constants in an effort to establish a relationship between these parameters. For completely non-polar fragments, the hydrophobicity is directly and linearly related to the volume or surface area. In the case of most fragments, however, the hydrophobicity is found to result from two factors, namely, a) a volume- or surface-related lipophilicity, and b) a lipophobicity effect, designated. The physical meaning of this parameter is unclear at present and may be related to hydration effects. The parameter may be of interest in QSAR studies. This is illustrated by an example in which the combined use of V and discriminates between the steric and lipophobic contributions of the partition coefficient to the biological activity.

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Year:  1981        PMID: 7196751

Source DB:  PubMed          Journal:  Arzneimittelforschung        ISSN: 0004-4172


  7 in total

1.  Substructure and whole molecule approaches for calculating log P.

Authors:  R Mannhold; H van de Waterbeemd
Journal:  J Comput Aided Mol Des       Date:  2001-04       Impact factor: 3.686

Review 2.  Modeling kinetics of subcellular disposition of chemicals.

Authors:  Stefan Balaz
Journal:  Chem Rev       Date:  2009-05       Impact factor: 60.622

3.  Lipophilicity of amino acids.

Authors:  H van de Waterbeemd; H Karajiannis; N El Tayar
Journal:  Amino Acids       Date:  1994-06       Impact factor: 3.520

4.  Pattern recognition study of QSAR substituent descriptors.

Authors:  H van de Waterbeemd; N el Tayar; P A Carrupt; B Testa
Journal:  J Comput Aided Mol Des       Date:  1989-06       Impact factor: 3.686

5.  Molecular lipophilicity potential, a tool in 3D QSAR: method and applications.

Authors:  P Gaillard; P A Carrupt; B Testa; A Boudon
Journal:  J Comput Aided Mol Des       Date:  1994-04       Impact factor: 3.686

Review 6.  Machine learning in chemoinformatics and drug discovery.

Authors:  Yu-Chen Lo; Stefano E Rensi; Wen Torng; Russ B Altman
Journal:  Drug Discov Today       Date:  2018-05-08       Impact factor: 7.851

Review 7.  Partitioning and lipophilicity in quantitative structure-activity relationships.

Authors:  J C Dearden
Journal:  Environ Health Perspect       Date:  1985-09       Impact factor: 9.031

  7 in total

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