Predicting ADMET Properties by Projecting onto Chemical Space?Benefits and Pitfalls
Abstract:The mechanisms behind ADME (absorption, distribution, metabolism, and excretion) related properties and toxicity endpoints are usually complex, and many are not fully understood. As a result, most ADMET predictive models are not based on theoretical principles, but are derived from experimental data. ADMET properties are best analyzed by projecting them onto the compounds of the training set. There are multiple advantages to projecting the ADMET properties from the problem domain to the chemical domain. Projection simplifies the problem, and avoids the entanglement of needing to invoke specific mechanisms. Projection focuses on the most important, and most tractable, aspect of the problem -- the related properties of the compounds themselves. In this review article, the general requirements of the chemical space to be projected are discussed, including the size and diversity of the training set and the accuracy of the biological measurements, and the process is illustrated using an analogue of a real projection. Also, the successes and pitfalls of the projection method in recent ADMET predictions are reviewed.
Document Type: Review Article
Affiliations: Discovery Chemistry, Hoffmann-La Roche Inc., 340 Kingsland Street, Nutley, New Jersey 07110, USA.
Publication date: 2005-04-01
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- Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design. Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, etc., providing excellent rationales for drug development.