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In Silico Prediction of Drug Properties

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Drug design has become inconceivable without the assistance of computer-aided methods. In this context in silico was chosen as designation to emphasize the relationship to in vitro and in vivo testing. Nowadays, virtual screening covers much more than estimation of solubility and oral bioavailability of compounds. Along with the challenge of parsing virtual compound libraries, the necessity to model more specific metabolic and toxicological aspects has emerged. Here, recent developments in prediction models are summarized, covering optimization problems in the fields of cytochrome P450 metabolism, blood-brain-barrier permeability, central nervous system activity, and blockade of the hERGpotassium channel. Aspects arising from the use of homology models and quantum chemical calculations are considered with respect to the biological functions. Furthermore, approaches to distinguish drug-like substances from nondrugs by the means of machine learning algorithms are compared in order to derive guidelines for the design of new agents with appropriate properties.

Keywords: blood-brain-barrier; classification algorithms; cytochrome P450; drug metabolism; drug-likeness; hERG-channel; virtual screening

Document Type: Research Article


Affiliations: Center for Bioinformatics, Saarland University, Campus Building C7.1, D-66123 Saarbruecken, Germany.

Publication date: January 1, 2009

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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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