Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles

Computational models that predict pharmacokinetic properties
are
critical to deprioritize drug candidates that emerge as hits in high-throughput
screening campaigns. We collected, curated, and integrated a database
of compounds tested in 12 major end points comprising over 10,000
unique molecules. We then employed these data to build and validate
binary quantitative structure–activity relationship (QSAR)
models. All trained models achieved a correct classification rate
above 0.60 and a positive predictive value above 0.50. To illustrate
their utility in drug discovery, we used these models to predict the
pharmacokinetic properties for drugs in the NCATS Inxight Drugs database.
In addition, we employed the developed models to predict the pharmacokinetic
properties of all compounds in the DrugBank. All models described
in this paper have been integrated and made publicly available via
the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.