Evolutionary computational methods to predict oral bioavailability QSPRs

Bains, W, Gilbert, R, Sviridenko, L, Gascon, J M, Scoffin, R, Birchall, K, Harvey, I and Caldwell, J (2002) Evolutionary computational methods to predict oral bioavailability QSPRs. Current Opinion in Drug Discovery and Development, 5 (1). pp. 44-51. ISSN 1367-6733

Full text not available from this repository.


This review discusses evolutionary and adaptive methods for predicting oral bioavailability (OB) from chemical structure. Genetic Programming (GP), a specific form of evolutionary computing, is compared with some other advanced computational methods for OB prediction. The results show that classifying drugs into 'high' and 'low' OB classes on the basis of their structure alone is solvable, and initial models are already producing output that would be useful for pharmaceutical research. The results also suggest that quantitative prediction of OB will be tractable. Critical aspects of the solution will involve the use of techniques that can: (i) handle problems with a very large number of variables (high dimensionality); (ii) cope with 'noisy' data; and (iii) implement binary choices to sub-classify molecules with behavior that are qualitatively different. Detailed quantitative predictions will emerge from more refined models that are hybrids derived from mechanistic models of the biology of oral absorption and the power of advanced computing techniques to predict the behavior of the components of those models in silico.

Item Type: Article
Additional Information: Originality. First major review of applying evolutionary methods for predicting oral bioavailibility, with significant results. Rigour. Work developing evolutionary methods trialled here at Sussex, and applying in the commercial pharmaceutical world. Significance.This work comprised around half of the IP of Amedis Pharmaceuticals Ltd, pharamaceutical start-up that attracted £4million capital before then being taken over. Outlet/Citations. Widely read journal in the area of this work. Google Scholar 9 citations..Web of Knowl 11 citations.
Schools and Departments: School of Engineering and Informatics > Informatics
Depositing User: Inman Harvey
Date Deposited: 06 Feb 2012 19:03
Last Modified: 13 Jun 2012 13:06
URI: http://sro.sussex.ac.uk/id/eprint/19089
📧 Request an update