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J Comput Chem

Title:		Application of ant colony optimization in development of models for prediction of anti-HIV-1 activity of HEPT derivatives
Author(s):		Zare-Shahabadi V; Abbasitabar F;
Address:		"Department of Chemistry, Islamic Azad University-Mahshahr Branch, Mahshahr, Iran. valizare@gmail.com"
Journal Title:		J Comput Chem
Year:		2010
Volume:		31
Issue:		12
Page Number:		2354 - 2362
DOI:		10.1002/jcc.21529
ISSN/ISBN:		1096-987X (Electronic) 0192-8651 (Linking)
Abstract:		"Quantitative structure-activity relationship models were derived for 107 analogs of 1-[(2-hydroxyethoxy) methyl]-6-(phenylthio)thymine, a potent inhibitor of the HIV-1 reverse transcriptase. The activities of these compounds were investigated by means of multiple linear regression (MLR) technique. An ant colony optimization algorithm, called Memorized_ACS, was applied for selecting relevant descriptors and detecting outliers. This algorithm uses an external memory based upon knowledge incorporation from previous iterations. At first, the memory is empty, and then it is filled by running several ACS algorithms. In this respect, after each ACS run, the elite ant is stored in the memory and the process is continued to fill the memory. Here, pheromone updating is performed by all elite ants collected in the memory; this results in improvements in both exploration and exploitation behaviors of the ACS algorithm. The memory is then made empty and is filled again by performing several ACS algorithms using updated pheromone trails. This process is repeated for several iterations. At the end, the memory contains several top solutions for the problem. Number of appearance of each descriptor in the external memory is a good criterion for its importance. Finally, prediction is performed by the elitist ant, and interpretation is carried out by considering the importance of each descriptor. The best MLR model has a training error of 0.47 log (1/EC(50)) units (R(2) = 0.90) and a prediction error of 0.76 log (1/EC(50)) units (R(2) = 0.88)"
Keywords:		"Algorithms Animals Anti-HIV Agents/*chemistry/*pharmacology Ants/*physiology Artificial Intelligence *Drug Design *Models, Biological Pheromones/physiology Predictive Value of Tests Quantitative Structure-Activity Relationship Reverse Transcriptase Inhibi;"
Notes:		"MedlineZare-Shahabadi, Vali Abbasitabar, Fatemeh eng 2010/06/25 J Comput Chem. 2010 Sep; 31(12):2354-62. doi: 10.1002/jcc.21529"