Literature DB >> 10052964

Self-organizing molecular field analysis: a tool for structure-activity studies.

D D Robinson1, P J Winn, P D Lyne, W G Richards.   

Abstract

Self-organizing molecular field analysis (SOMFA) is a novel technique for three-dimensional quantitative structure-activity relations (3D-QSAR). It is simple and intuitive in concept and avoids the complex statistical tools and variable selection procedures favored by other methods. Our calculations show the method to be as predictive as the best 3D-QSAR methods available. Importantly, steric and electrostatic maps can be produced to aid the molecular design process by highlighting important molecular features. The simplicity of the technique leaves scope for further development, particularly with regard to handling molecular alignment and conformation selection. Here, the method has been used to predict the corticosteroid-binding globulin binding affinity of the "benchmark" steroids, expanded from the usual 31 compounds to 43 compounds. Test predictions have also been performed on a set of sulfonamide endothelin inhibitors.

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Year:  1999        PMID: 10052964     DOI: 10.1021/jm9810607

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  15 in total

1.  Internally defined distances in 3D-quantitative structure-activity relationships.

Authors:  Christian Th Klein; Norbert Kaiblinger; Peter Wolschann
Journal:  J Comput Aided Mol Des       Date:  2002-02       Impact factor: 3.686

2.  Activity landscape analysis of novel 5α-reductase inhibitors.

Authors:  J Jesús Naveja; Francisco Cortés-Benítez; Eugene Bratoeff; José L Medina-Franco
Journal:  Mol Divers       Date:  2016-02-01       Impact factor: 2.943

3.  Validation tools for variable subset regression.

Authors:  Knut Baumann; Nikolaus Stiefl
Journal:  J Comput Aided Mol Des       Date:  2004 Jul-Sep       Impact factor: 3.686

4.  3D-QSAR illusions.

Authors:  Arthur M Doweyko
Journal:  J Comput Aided Mol Des       Date:  2004 Jul-Sep       Impact factor: 3.686

5.  Quantitative Series Enrichment Analysis (QSEA): a novel procedure for 3D-QSAR analysis.

Authors:  Bernd Wendt; Richard D Cramer
Journal:  J Comput Aided Mol Des       Date:  2008-02-27       Impact factor: 3.686

6.  Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions.

Authors:  Kyaw-Zeyar Myint; Lirong Wang; Qin Tong; Xiang-Qun Xie
Journal:  Mol Pharm       Date:  2012-08-31       Impact factor: 4.939

Review 7.  Recent advances in fragment-based QSAR and multi-dimensional QSAR methods.

Authors:  Kyaw Zeyar Myint; Xiang-Qun Xie
Journal:  Int J Mol Sci       Date:  2010-10-08       Impact factor: 5.923

8.  Quantitative structure-activity relationships from optimised ab initio bond lengths: steroid binding affinity and antibacterial activity of nitrofuran derivatives.

Authors:  P J Smith; P L A Popelier
Journal:  J Comput Aided Mol Des       Date:  2004-02       Impact factor: 3.686

9.  Receptor independent and receptor dependent CoMSA modeling with IVE-PLS: application to CBG benchmark steroids and reductase activators.

Authors:  Tomasz Magdziarz; Pawel Mazur; Jaroslaw Polanski
Journal:  J Mol Model       Date:  2008-10-21       Impact factor: 1.810

10.  Ligand intramolecular motions in ligand-protein interaction: ALPHA, a novel dynamic descriptor and a QSAR study with extended steroid benchmark dataset.

Authors:  Kari Tuppurainen; Marja Viisas; Mikael Peräkylä; Reino Laatikainen
Journal:  J Comput Aided Mol Des       Date:  2004-03       Impact factor: 3.686

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