Recent developments of in silico predictions of intestinal absorption and oral bioavailability

Tingjun Hou, Youyong Li, Wei Zhang, Junmei Wang

Research output: Contribution to journalReview articlepeer-review

78 Scopus citations


Among the absorption, distribution, metabolism, elimination, and toxicity properties (ADMET), unfavorable oral bioavailability is indeed an important reason for stopping further development of the drug candidates. Thus, predictions of oral bioavailability and bioavailability-related properties, especially intestinal absorption are areas in need of progress to aid pharmaceutical drug development. In this article, we review recent developments in the prediction of passive intestinal absorption and oral bioavailability. The advances in the datasets used for model building, the molecular descriptors, the prediction models, and the statistical modeling techniques, are summarized. Furthermore, we compared the performance of one machine learning method, support vector machines (SVM), and one traditional classification method, recursive partitioning (RP), on the predictions of passive absorption. Our comparisons demonstrate that the complex machine learning method could give better predictions than the traditional approach. Finally we discuss the current challenges that remain to be addressed.

Original languageEnglish (US)
Pages (from-to)497-506
Number of pages10
JournalCombinatorial Chemistry and High Throughput Screening
Issue number5
StatePublished - Jun 2009


  • Bioavailability
  • In silico prediction
  • Intestinal absorption
  • Machine learning

ASJC Scopus subject areas

  • Drug Discovery
  • Computer Science Applications
  • Organic Chemistry


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