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Feature Set Selection in QSAR of 1-[(2-Hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) Analogues by Using Swarm Intelligence

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Summary.

A binary particle swarm optimization (PSO) is adopted for major influence descriptors selection in quantitative structure-activity relationship (QSAR) of a large data set of 1-[(2-Hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) analogues due to its simplicity, speed, and consistency. It has an embedded mechanism, which is based on social behavior of sharing information within a group and experience of each particle that could reach a near-optimal solution. The modified particle swarm optimization is then combined with the neural networks (NNs) for its universal approximating property to generate a QSAR model with the selected features. Since the quality of chosen features also depend on the reliability of the QSAR model, two effective and efficient algorithms, the Levenberg-Marquardt backpropagation (PSO–LMBP) and the PSO (PSO–PSO), were used instead of the classical backpropagation (gradient descent method). The Pearson correlation is employed as the fitness function for the predictive ability of the obtained QSAR model from the selected features. Experimental results reveal that the PSO is a useful tool for feature set selection in QSAR of a large data set of HEPT analogues.

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Authors and Affiliations

  1. School of Technology, Shinawatra University, Bangkok, Thailand

    Chakguy Prakasvudhisarn

  2. Sirindhorn International Institute of Technology (SIIT), Thammasat University, Pathum Thani, Thailand

    Luckhana Lawtrakul

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

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Prakasvudhisarn, C., Lawtrakul, L. Feature Set Selection in QSAR of 1-[(2-Hydroxyethoxy)methyl]-6-(phenylthio)thymine (HEPT) Analogues by Using Swarm Intelligence. Monatsh Chem 139, 197–211 (2008). https://doi.org/10.1007/s00706-007-0773-4

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  • DOI: https://doi.org/10.1007/s00706-007-0773-4

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