Literature DB >> 7707309

Cross-validated R2-guided region selection for comparative molecular field analysis: a simple method to achieve consistent results.

S J Cho1, A Tropsha.   

Abstract

Comparative Molecular Field Analysis (CoMFA) is one of the most powerful modern tools for quantitative structure-activity relationship studies. The CoMFA predictability is conventionally characterized by a cross-validated correlation coefficient R2 (q2). Our CoMFA investigation of 4 datasets, including 7 cephalotaxine esters, 20 5-HT1A receptor ligands, 59 inhibitors of HIV protease, and 21 steroids reveals that the q2 value is sensitive to the overall orientation of superimposed molecules on a computer terminal and can vary by as much as 0.5q2 units when the orientation is varied by systematic rotation. To optimize CoMFA, we have developed a new routine, cross-validated R2-guided region selection (q2-GRS). We first subdivide the rectangular lattice obtained initially with conventional CoMFA into 125 small boxes and perform 125 independent analyses using probe atoms placed within each box with the step size of 1.0 A. We then select only those small boxes for which a q2 is higher than a specified optimal cutoff value. Finally, we repeat CoMFA with the union of small boxes selected at the previous step. Four datasets described above were used to validate this new q2-GRS routine. In each case we have obtained an orientation-independent, high q2, exceeding the one obtained with the conventional CoMFA. This method shall be used routinely in the future CoMFA studies to guarantee the reproducibility of the reported q2 values.

Entities:  

Mesh:

Substances:

Year:  1995        PMID: 7707309     DOI: 10.1021/jm00007a003

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


  45 in total

1.  Evaluation of the EVA descriptor for QSAR studies: 3. The use of a genetic algorithm to search for models with enhanced predictive properties (EVA_GA).

Authors:  D B Turner; P Willett
Journal:  J Comput Aided Mol Des       Date:  2000-01       Impact factor: 3.686

2.  Global 3D-QSAR methods: MS-WHIM and autocorrelation.

Authors:  E Gancia; G Bravi; P Mascagni; A Zaliani
Journal:  J Comput Aided Mol Des       Date:  2000-03       Impact factor: 3.686

3.  Evaluation of a novel molecular vibration-based descriptor (EVA) for QSAR studies: 2. Model validation using a benchmark steroid dataset.

Authors:  D B Turner; P Willett; A M Ferguson; T W Heritage
Journal:  J Comput Aided Mol Des       Date:  1999-05       Impact factor: 3.686

4.  An extensive ecdysteroid CoMFA.

Authors:  L Dinan; R E Hormann; T Fujimoto
Journal:  J Comput Aided Mol Des       Date:  1999-03       Impact factor: 3.686

5.  Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection.

Authors:  Alexander Golbraikh; Alexander Tropsha
Journal:  J Comput Aided Mol Des       Date:  2002 May-Jun       Impact factor: 3.686

6.  Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection.

Authors:  Alexander Golbraikh; Alexander Tropsha
Journal:  Mol Divers       Date:  2002       Impact factor: 2.943

7.  3D QSAR (COMFA) of a series of potent and highly selective VLA-4 antagonists.

Authors:  Juswinder Singh; Herman van Vlijmen; Wen-Chemg Lee; Yusheng Liao; Ko-Chung Lin; Humayun Ateeq; Julio Cuervo; Craig Zimmerman; Charles Hammond; Michael Karpusas; Rex Palmer; Tapan Chattopadhyay; Steven P Adams
Journal:  J Comput Aided Mol Des       Date:  2002-03       Impact factor: 3.686

8.  3D-QSAR and molecular modeling of HIV-1 integrase inhibitors.

Authors:  Mahindra T Makhija; Vithal M Kulkarni
Journal:  J Comput Aided Mol Des       Date:  2002-03       Impact factor: 3.686

9.  CoMFA analysis of tgDHFR and rlDHFR based on antifolates with 6-5 fused ring system using the all-orientation search (AOS) routine and a modified cross-validated r(2)-guided region selection (q(2)-GRS) routine and its initial application.

Authors:  Aleem Gangjee; Xin Lin; Lisa R Biondo; Sherry F Queener
Journal:  Bioorg Med Chem       Date:  2010-01-06       Impact factor: 3.641

10.  Correlation between the predicted and the observed biological activity of the symmetric and nonsymmetric cyclic urea derivatives used as HIV-1 protease inhibitors. A 3D-QSAR-CoMFA method for new antiviral drug design.

Authors:  Speranta Avram; I Svab; C Bologa; Maria-Luiza Flonta
Journal:  J Cell Mol Med       Date:  2003 Jul-Sep       Impact factor: 5.310

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.