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Important Molecular Descriptors Selection Using Self Tuned Reweighted Sampling Method for Prediction of Antituberculosis Activity

Doreswamy,C. Vastrad

Published 2014 in arXiv.org

ABSTRACT

In this paper, a new descriptor selection method for selecting an optimal combination of important descriptors of sulfonamide derivatives data, named self tuned reweighted sampling (STRS), is developed. descriptors are defined as the descriptors with large absolute coefficients in a multivariate linear regression model such as partial least squares(PLS). In this study , the absolute values of regression coefficients of PLS model are used as an index for evaluating the importance of each descriptor Then, based on the importance level of each descriptor, STRS sequentially selects N subsets of descriptors from N Monte Carlo (MC) sampling runs in an iterative and competitive manner. In each sampling run, a fixed ratio (e.g. 80%) of samples is first randomly selected to establish a regresson model. Next, based on the regression coefficients, a two-step procedure including rapidly decreasing function (RDF) based enforced descriptor selection and self tuned sampling (STS) based competitive descriptor selection is adopted to select the important descriptorss. After running the loops, a number of subsets of descriptors are obtained and root mean squared error of cross validation (RMSECV) of PLS models established with subsets of descriptors is computed. The subset of descriptors with the lowest RMSECV is considered as the optimal descriptor subset. The performance of the proposed algorithm is evaluated by sulfanomide derivative dataset. The results reveal an good characteristic of STRS that it can usually locate an optimal combination of some important descriptors which are interpretable to the biologically of interest. Additionally, our study shows that better prediction is obtained by STRS when compared to full descriptor set PLS modeling, Monte Carlo uninformative variable elimination (MC-UVE). Compared to the partial least squares regression models based on full descriptor set and descriptors selected by MC-UVE, the performance of STRS with PLS model was better, with higher determination coefficient for test ( �� �� ) ���� 0.8758 , and lower root mean square error of prediction of 0.1676. Based on the results, it was concluded that Sulfonamide with STRS methods seem to be a rapid and effective alternative to the classical methods for the prediction of antituberculosis activity.

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    arXiv.org

  • Publication date

    2014-02-21

  • Fields of study

    Medicine, Chemistry, Mathematics, Computer Science

  • Identifiers
    arXiv 1402.5360
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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