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

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