title: Efficient identification of novel leads by dynamic focused screening: PDK1 case stud.
abstract: A dynamic, focused screening strategy that utilized a limited but diversified set of target-specific compounds was explored as an efficient means for the identification of inhibitors of the protein kinase PDK1. Approximately 21,500 compounds, including a 19,000 molecule kinase-focused compound collection (KFCC), were screened at two concentrations to identify initial leads. The KFCC included several empirically-derived, general kinase libraries and molecules chosen by PDK1-specific virtual screens. As was expected, this initial screen mostly identified potent leads with limited novelty. In order to overcome this limitation, the data from the screen were used to drive several rounds of a customized iterative focused screening (IFS) campaign. A machine-learning technique was used to build a predictive model to identify compounds to be screened in subsequent rounds. Molecules deemed not to be novel were removed from the training set for the next round, which allowed this campaign to progressively walk away from the chemical space covered by the KFCC. This resulted in the identification of PDK1 inhibitors which are uniquely different from publicly known chemotypes after just three rounds of screenings. A retrospective analysis of this IFS approach against an ultra-high throughput screen (uHTS) indicated that while uHTS is still the most prolific paradigm for lead identification, this dynamic, focused screening approach was successful in discovering novel scaffolds for a medicinal chemistry effort. Finally, a theoretical optimization suggested the dynamic, focused screening approaches could provide either a complementary or alternative approach to uHTS for the efficient and rapid lead identification.
authors.affiliation: Drug Design and Optimization, Boston, MA 02115, USA.
authors.forename: Dongyu
authors.initials: D
authors.lastname: Sun
authors.forename: Joon
authors.initials: J
authors.lastname: Jung
authors.forename: Thomas S
authors.initials: TS
authors.lastname: Rush
authors.forename: Zangwei
authors.initials: Z
authors.lastname: Xu
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Weber
authors.forename: Ekaterina
authors.initials: E
authors.lastname: Bobkova
authors.forename: Alan
authors.initials: A
authors.lastname: Northrup
authors.forename: Ilona
authors.initials: I
authors.lastname: Kariv
mesh_terms: D064413:3-Phosphoinositide-Dependent Protein Kinases; D001185:Artificial Intelligence; D015195:Drug Design; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D047428:Protein Kinase Inhibitors / Q000737:chemistry / Q000494:pharmacology*; D017346:Protein Serine-Threonine Kinases / Q000037:antagonists & inhibitors* / Q000378:metabolism*; D013329:Structure-Activity Relationship
chemical_list: D047428:Protein Kinase Inhibitors; D064413:3-Phosphoinositide-Dependent Protein Kinases; C578562:PDPK1 protein, human; D017346:Protein Serine-Threonine Kinases