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Evolutionary computational methods to predict oral bioavailability QSPRs

journal contribution
posted on 2023-06-07, 21:52 authored by W Bains, R Gilbert, L Sviridenko, J M Gascon, R Scoffin, K Birchall, Inman HarveyInman Harvey, J Caldwell
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.

History

Publication status

  • Published

Journal

Current Opinion in Drug Discovery and Development

ISSN

1367-6733

Publisher

Thomson Reuters

Issue

1

Volume

5

Page range

44-51

Department affiliated with

  • Informatics Publications

Notes

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.

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2012-02-06

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