The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine

H. X. Liu, R. J. Hu, R. S. Zhang, X. J. Yao, M. C. Liu, Z. D. Hu, B. T. Fan

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)

Abstract

Support vector machine (SVM), as a novel machine learning technique, was used for the prediction of the human oral absorption for a large and diverse data set using the five descriptors calculated from the molecular structure alone. The molecular descriptors were selected by heuristic method (HM) implemented in CODESSA. At the same time, in order to show the influence of different molecular descriptors on absorption and to well understand the absorption mechanism, HM was used to build several multivariable linear models using different numbers of molecular descriptors. Both the linear and non-linear model can give satisfactory prediction results: the square of correlation coefficient R2 was 0.78 and 0.86 for the training set, and 0.70 and 0.73 for the test set respectively. In addition, this paper provides a new and effective method for predicting the absorption of the drugs from their structures and gives some insight into structural features related to the absorption of the drugs.

Original languageEnglish
Pages (from-to)33-46
Number of pages14
JournalJournal of Computer-Aided Molecular Design
Volume19
Issue number1
DOIs
Publication statusPublished - Jan 2005
Externally publishedYes

Keywords

  • Heuristic method
  • Humanoral absorption
  • QSPR/QSAR
  • Support vector machine

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