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Current Drug Discovery Technologies

Editor-in-Chief

ISSN (Print): 1570-1638
ISSN (Online): 1875-6220

Research Article

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques

Author(s): Rajnish Kumar*, Anju Sharma, Mohammed Haris Siddiqui and Rajesh Kumar Tiwari

Volume 14, Issue 4, 2017

Page: [244 - 254] Pages: 11

DOI: 10.2174/1570163814666170404160911

Price: $65

Abstract

Background: Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches.

Methods: Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds.

Results: Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively.

Conclusion: Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development.

Keywords: Absorption, artificial intelligence, drug, pharmacokinetics, prediction.

Graphical Abstract

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