Score function of violations and best cutpoint to identify druggable molecules and associated disease targets

Hudson, I, Leemaqz, S, Shafi, S and Abell, A 2017, 'Score function of violations and best cutpoint to identify druggable molecules and associated disease targets', in Syme, G., Hatton MacDonald, D., Fulton, B. and Piantadosi, J. (ed.) Proceedings of the 22nd International Congress on Modelling and Simulation (MODSIM 2017), Hobart, Tasmania, 3-8 December 2017, pp. 487-493.


Document type: Conference Paper
Collection: Conference Papers

Title Score function of violations and best cutpoint to identify druggable molecules and associated disease targets
Author(s) Hudson, I
Leemaqz, S
Shafi, S
Abell, A
Year 2017
Conference name MODSIM 2017
Conference location Hobart, Tasmania
Conference dates 3-8 December 2017
Proceedings title Proceedings of the 22nd International Congress on Modelling and Simulation (MODSIM 2017)
Editor(s) Syme, G., Hatton MacDonald, D., Fulton, B. and Piantadosi, J.
Publisher The Modelling and Simulation Society of Australia and New Zealand
Place of publication Australia
Start page 487
End page 493
Total pages 7
Abstract Predicting druggability and prioritising certain disease modifying targets is critical in drug discovery. Expanding the spectrum of disease-relevant targets to pharmacological manipulation is vital to reducing morbidity and mortality. We test a druggability rule, based on 10 molecular parameters (scores counting violations, denoted by score10), which uses cutpoints for each molecular parameter based on mixture clustering discriminant analysis (MC/DA) (Hudson et al., 2014). A total of 1279 small molecules from the DrugBank chem-informatics database (Knox et al., 2011), combining detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with drug disease target information, were analysed and these were shown to be aligned with 173 targets. The score10 function comprised 4 traditional parameters of the rule of five (Ro5) (Lipinski, 2016), plus 5 extra parameters (polar surface area PSA, number of rotatable bonds, rings and halogens, N and O atoms) with an extra candidate of lipophicility, log D (the distribution coefficient) recently suggested by Bhal et al., 2007 as a possible preferable predictor for permeation (Zafar, Hudson et al., 2016, 2013;) to Lipinski's traditional partition coefficient, Log P, a predictor for permeation.
Subjects Biostatistics
Keyword(s) druggability rules
beyond the rule of five (bRo5)
disease targets
machine learning
ISBN 9780987214379
Versions
Version Filter Type
Access Statistics: 3 Abstract Views  -  Detailed Statistics
Created: Fri, 05 Jul 2019, 12:33:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us