Literature DB >> 24437630

Template CoMFA: the 3D-QSAR Grail?

Richard D Cramer1, Bernd Wendt.   

Abstract

Template CoMFA, a novel alignment methodology for training or test set structures in 3D-QSAR, is introduced. Its two most significant advantages are its complete automation and its ability to derive a single combined model from multiple structural series affecting a biological target. Its only two inputs are one or more "template" structures having 3D coordinates that share some Cartesian space, as may result from X-ray crystallography or pharmacophoric hypothesis, and one or more connectivity-only SAR tables associated with a common target. Template CoMFA also overcomes the major disadvantages of both existing 3D-QSAR alignment methodologies, specifically the tedium and subjectivity of familiar ad hoc approaches, and the awkwardness, occasional physicochemical heresies, and structural scope limitations of the purely topomer approach. The template CoMFA algorithms are described, and two of its application classes are presented. The first class, general models of binding to factor Xa and P38 map kinase, uses crystallographic structures as templates, with the encouraging result that the statistical qualities of each of these two combined models are equivalent to those of their constituent individual series models. The second, 15 data sets originally collected for validation of topomer CoMFA, with arbitrary structures as templates, confirms that the modeling power of template CoMFA resembles that of its predecessors.

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Year:  2014        PMID: 24437630     DOI: 10.1021/ci400696v

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  7 in total

1.  Challenging the gold standard for 3D-QSAR: template CoMFA versus X-ray alignment.

Authors:  Bernd Wendt; Richard D Cramer
Journal:  J Comput Aided Mol Des       Date:  2014-06-17       Impact factor: 3.686

2.  Modeling MEK4 Kinase Inhibitors through Perturbed Electrostatic Potential Charges.

Authors:  Rama K Mishra; Kristine K Deibler; Matthew R Clutter; Purav P Vagadia; Matthew O'Connor; Gary E Schiltz; Raymond Bergan; Karl A Scheidt
Journal:  J Chem Inf Model       Date:  2019-10-14       Impact factor: 4.956

3.  Template CoMFA Generates Single 3D-QSAR Models that, for Twelve of Twelve Biological Targets, Predict All ChEMBL-Tabulated Affinities.

Authors:  Richard D Cramer
Journal:  PLoS One       Date:  2015-06-12       Impact factor: 3.240

4.  Docking and quantitative structure-activity relationship of bi-cyclic heteroaromatic pyridazinone and pyrazolone derivatives as phosphodiesterase 3A (PDE3A) inhibitors.

Authors:  Camila Muñoz-Gutiérrez; Daniela Cáceres-Rojas; Francisco Adasme-Carreño; Iván Palomo; Eduardo Fuentes; Julio Caballero
Journal:  PLoS One       Date:  2017-12-07       Impact factor: 3.240

5.  Teaching and Learning Computational Drug Design: Student Investigations of 3D Quantitative Structure-Activity Relationships through Web Applications.

Authors:  Rino Ragno; Valeria Esposito; Martina Di Mario; Stefano Masiello; Marco Viscovo; Richard D Cramer
Journal:  J Chem Educ       Date:  2020-06-23       Impact factor: 2.979

6.  Quantum Artificial Neural Network Approach to Derive a Highly Predictive 3D-QSAR Model for Blood-Brain Barrier Passage.

Authors:  Taeho Kim; Byoung Hoon You; Songhee Han; Ho Chul Shin; Kee-Choo Chung; Hwangseo Park
Journal:  Int J Mol Sci       Date:  2021-10-12       Impact factor: 5.923

7.  Nonadditivity in public and inhouse data: implications for drug design.

Authors:  D Gogishvili; E Nittinger; C Margreitter; C Tyrchan
Journal:  J Cheminform       Date:  2021-07-02       Impact factor: 5.514

  7 in total

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