File(s) not publicly available
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 CaldwellThis 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 DevelopmentISSN
1367-6733Publisher
Thomson ReutersIssue
1Volume
5Page range
44-51Department 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
2012-02-06Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC