Quantitative bioactivity prediction and pharmacophore identification for benzotriazine derivatives using the electron conformational-genetic algorithm in QSAR

Quantitative bioactivity prediction and pharmacophore identification for benzotriazine derivatives using the electron conformational-genetic algorithm in QSAR

The electron conformational-genetic algorithm (EC-GA), a sophisticated hybrid approach combining the GA and EC methods, has been employed for a 4D-QSAR procedure to identify the pharmacophore for benzotriazines as sarcoma inhibitors and for quantitative prediction of activity. The calculated geometr...

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Bibliographic Details
Published in:SAR and QSAR in environmental research Vol. 22; no. 3-4; pp. 217 - 238
Main Authors: Şahin, K., Sarıpınar, E., Yanmaz, E., Geçen, N.
Format: Journal Article
Language:English
Published: England Taylor & Francis Group 01-06-2011
Subjects:
Algorithms
Antineoplastic Agents - chemistry
Antineoplastic Agents - pharmacology
benzotriazines
Computer Simulation
drug design
Electrons
genetic algorithm
Humans
Models, Chemical
molecular modelling
pharmacophore
Quantitative Structure-Activity Relationship
Triazines - chemistry
Triazines - pharmacology
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Summary:The electron conformational-genetic algorithm (EC-GA), a sophisticated hybrid approach combining the GA and EC methods, has been employed for a 4D-QSAR procedure to identify the pharmacophore for benzotriazines as sarcoma inhibitors and for quantitative prediction of activity. The calculated geometry and electronic structure parameters of every atom and bond of each molecule are arranged in a matrix described as the electron-conformational matrix of contiguity (ECMC). By comparing the ECMC of one of the most active compounds with other ECMCs we were able to obtain the features of the pharmacophore responsible for the activity, as submatrices of the template known as electron conformational submatrices of activity. The GA was used to select the most important descriptors and to predict the theoretical activity of training and test sets. The predictivity of the model was internally validated. The best QSAR model was selected, having r 2  = 0.9008, standard error = 0.0510 and cross-validated squared correlation coefficient, q 2  = 0.8192.
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ISSN:1062-936X
1029-046X
DOI:10.1080/1062936X.2010.548341