Literature DB >> 11277729

Application of BCUT metrics and genetic algorithm in binary QSAR analysis.

H Gao1.   

Abstract

The application of three-dimensional H-suppressed BCUT metrics (BCUTs) in binary QSAR analysis was investigated using carbonic anhydrase II inhibitors and estrogen receptor ligands as test cases. Variable selection was accomplished with a genetic algorithm (GA). Highly predictive binary QSAR models were obtained for both sets of compounds within 200 GA generations. The derived binary QSAR models were validated with two sets of compounds not included in the training sets. The results indicate that BCUTs are very useful molecular descriptors, and the genetic algorithm is a very efficient variable selection tool in binary QSAR analysis.

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Year:  2001        PMID: 11277729     DOI: 10.1021/ci000306p

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  6 in total

1.  Estimation of aqueous solubility of organic compounds with QSPR approach.

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Journal:  Pharm Res       Date:  2002-04       Impact factor: 4.200

Review 2.  Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).

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Journal:  Mol Divers       Date:  2010-03-20       Impact factor: 2.943

3.  Genetic algorithms and self-organizing maps: a powerful combination for modeling complex QSAR and QSPR problems.

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Journal:  J Comput Aided Mol Des       Date:  2004 Jul-Sep       Impact factor: 3.686

4.  Compound acquisition and prioritization algorithm for constructing structurally diverse compound libraries.

Authors:  Chao Ma; John S Lazo; Xiang-Qun Xie
Journal:  ACS Comb Sci       Date:  2011-04-18       Impact factor: 3.784

5.  A classification study of human β₃-adrenergic receptor agonists using BCUT descriptors.

Authors:  Ming Hao; Yan Li; Yonghua Wang; Shuwei Zhang
Journal:  Mol Divers       Date:  2011-05-31       Impact factor: 2.943

6.  Toward the prediction of FBPase inhibitory activity using chemoinformatic methods.

Authors:  Ming Hao; Shuwei Zhang; Jieshan Qiu
Journal:  Int J Mol Sci       Date:  2012-06-07       Impact factor: 6.208

  6 in total

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