Literature DB >> 12540237

Topomer CoMFA: a design methodology for rapid lead optimization.

Richard D Cramer1.   

Abstract

To provide an objective QSAR methodology that might accelerate lead optimization, the CoMFA and topomer technologies have been merged, with surprisingly good results. A series of input structures are each broken into two or more fragments at central acyclic single bonds, while removing any core fragment structurally common to the entire series. Standard topomer 3D models are automatically constructed for each fragment, and a set of steric and electrostatic fields ("CoMFA column") is generated for each set of topomers. Application of "topomer CoMFA" to 15 3D-QSAR analyses taken from the literature (847 structures) were all successful, with an average q(2) of 0.520 (literature average q(2) = 0.636) and an average standard deviation of true prediction (SDEP) of 0.688 (literature average SDEP = 0.553) for 133 structures. Topomer CoMFA results are particularly promising as queries into virtual libraries already composed of topomer structures, to directly seek structures having increased potency. Accordingly, in 13 of the 15 such "topomer CoMFA searches" attempted, combinations of commercially offered fragments were retrieved that were predicted to be more potent than any structure described in the original publication (average predicted potency increase = 20 x), showing in principle how optimization could occur.

Mesh:

Substances:

Year:  2003        PMID: 12540237     DOI: 10.1021/jm020194o

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


  32 in total

1.  R-group template CoMFA combines benefits of "ad hoc" and topomer alignments using 3D-QSAR for lead optimization.

Authors:  Richard D Cramer
Journal:  J Comput Aided Mol Des       Date:  2012-06-04       Impact factor: 3.686

2.  Tautomers and topomers: challenging the uncertainties of direct physicochemical modeling.

Authors:  Richard D Cramer
Journal:  J Comput Aided Mol Des       Date:  2010-03-21       Impact factor: 3.686

3.  Negative allosteric modulators that target human alpha4beta2 neuronal nicotinic receptors.

Authors:  Brandon J Henderson; Ryan E Pavlovicz; Jerad D Allen; Tatiana F González-Cestari; Crina M Orac; Andrew B Bonnell; Michael X Zhu; R Thomas Boyd; Chenglong Li; Stephen C Bergmeier; Dennis B McKay
Journal:  J Pharmacol Exp Ther       Date:  2010-06-15       Impact factor: 4.030

4.  QMOD: physically meaningful QSAR.

Authors:  Ajay N Jain
Journal:  J Comput Aided Mol Des       Date:  2010-08-19       Impact factor: 3.686

Review 5.  Pushing the boundaries of 3D-QSAR.

Authors:  Richard D Cramer; Bernd Wendt
Journal:  J Comput Aided Mol Des       Date:  2007-01-26       Impact factor: 3.686

6.  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

7.  A ligand's-eye view of protein binding.

Authors:  Robert D Clark
Journal:  J Comput Aided Mol Des       Date:  2008-01-24       Impact factor: 3.686

8.  Herman Skolnik award symposium honoring Yvonne Martin.

Authors:  Wendy A Warr
Journal:  J Comput Aided Mol Des       Date:  2009-12-10       Impact factor: 3.686

9.  2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors.

Authors:  Bing Niu; Manman Zhao; Qiang Su; Mengying Zhang; Wei Lv; Qin Chen; Fuxue Chen; Dechang Chu; Dongshu Du; Yuhui Zhang
Journal:  Mol Divers       Date:  2017-03-09       Impact factor: 2.943

Review 10.  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

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.