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Correlation Studies of HEPT Derivatives Using Swarm Intelligence and Support Vector Machines

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Summary.

Two novel algorithms based on particle swarm optimization (PSO) and support vector machine (SVM) have been employed to obtain predictive QSAR models of anti-HIV-1 activity of HEPT derivatives. The results obtained by using the adopted PSO and SVM for structure-activity correlation determination were in close agreement with previous multiple linear regression models, which are reasonably satisfying, based on both statistical significance and predictive ability.

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Authors and Affiliations

  1. Department of Common and Graduate Studies and School of Manufacturing Systems and Mechanical Engineering, Sirindhorn International Institute of Technology (SIIT), Thammasat University, P.O. Box 22 Thammasat Rangsit Post Office, 12121, Pathumthani, Thailand

    Luckhana Lawtrakul & Chakguy Prakasvudhisarn

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  1. Luckhana Lawtrakul
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  2. Chakguy Prakasvudhisarn
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Correspondence to Luckhana Lawtrakul or Chakguy Prakasvudhisarn.

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Lawtrakul, L., Prakasvudhisarn, C. Correlation Studies of HEPT Derivatives Using Swarm Intelligence and Support Vector Machines. Monatsh. Chem. 136, 1681–1691 (2005). https://doi.org/10.1007/s00706-005-0357-0

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  • DOI: https://doi.org/10.1007/s00706-005-0357-0

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