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Recent Advances in Computational Prediction of Drug Absorption and Permeability in Drug Discovery

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Approximately 40%-60% of developing drugs failed during the clinical trials because of ADME/Tox deficiencies. Virtual screening should not be restricted to optimize binding affinity and improve selectivity; and the pharmacokinetic properties should also be included as important filters in virtual screening. Here, the current development in theoretical models to predict drug absorption-related properties, such as intestinal absorption, Caco-2 permeability, and blood-brain partitioning are reviewed. The important physicochemical properties used in the prediction of drug absorption, and the relevance of predictive models in the evaluation of passive drug absorption are discussed. Recent developments in the prediction of drug absorption, especially with the application of new machine learning methods and newly developed software are also discussed. Future directions for research are outlined.
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Keywords: ADME; Caco-2 monolayer; QSAR; blood-brain partitioning (BBB); drug adsorption; logBB; permeability

Document Type: Research Article

Affiliations: Department of Chemistry and Biochemistry, Center for Theoretical Biological Physics, University of California at San Diego, La Jolla, CA 92093, USA.

Publication date: 2006-09-01

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  • Current Medicinal Chemistry covers all the latest and outstanding developments in medicinal chemistry and rational drug design. Each issue contains a series of timely in-depth reviews written by leaders in the field covering a range of the current topics in medicinal chemistry. Current Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be kept informed and up-to-date with the latest and most important developments.
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