Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories-Reference-Cited by-同舟云学术

Enhanced replacement method integration with genetic algorithms populations in QSAR and QSPR theories

Published:2015-12 Issue: Volume:149 Page:117-122
ISSN:0169-7439
Container-title:Chemometrics and Intelligent Laboratory Systems
language:en
Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Mercader Andrew G.,Duchowicz Pablo R.

Funder

National Research Council of Argentina

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Reference38 articles.

1. Exploring QSAR: Fundamentals and Applications in Chemistry and Biology;Hansch,1995

2. Consensus ranking approach to understanding the underlying mechanism with QSAR;Shao;J. Chem. Inf. Model.,2010

3. J.R. Bajorath, Identification of descriptors capturing compound class-specific features by mutual information analysis;Wassermann;J. Chem. Inf. Model.,1935

4. Comparative analysis of QSAR models for predicting pKa of organic oxygen acids and nitrogen bases from molecular structure;Yu;J. Chem. Inf. Model.,2010

5. Evaluation of quantitative structure-activity relationship modeling strategies: local and global models;Helgee;J. Chem. Inf. Model.,2010

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