title: Normalizing molecular docking rankings using virtually generated decoys.
abstract: Drug discovery research often relies on the use of virtual screening via molecular docking to identify active hits in compound libraries. An area for improvement among many state-of-the-art docking methods is the accuracy of the scoring functions used to differentiate active from nonactive ligands. Many contemporary scoring functions are influenced by the physical properties of the docked molecule. This bias can cause molecules with certain physical properties to incorrectly score better than others. Since variation in physical properties is inevitable in large screening libraries, it is desirable to account for this bias. In this paper, we present a method of normalizing docking scores using virtually generated decoy sets with matched physical properties. First, our method generates a set of property-matched decoys for every molecule in the screening library. Each library molecule and its decoy set are docked using a state-of-the-art method, producing a set of raw docking scores. Next, the raw docking score of each library molecule is normalized against the scores of its decoys. The normalized score represents the probability that the raw docking score was drawn from the background distribution of nonactive property-matched decoys. Assuming that the distribution of scores of active molecules differs from the nonactive score distribution, we expect that the score of an active compound will have a low probability of having been drawn from the nonactive score distribution. In addition to the use of decoys in normalizing docking scores, we suggest that decoy sets may be a useful tool to evaluate, improve, or develop scoring functions. We show that by analyzing docking scores of library molecules with respect to the docking scores of their virtually generated property-matched decoys, one can gain insight into the advantages, limitations, and reliability of scoring functions.
authors.affiliation: Department of Computer Science, University of Toronto, Toronto, Ontario, Canada. izharw@cs.toronto.edu
authors.forename: Izhar
authors.initials: I
authors.lastname: Wallach
authors.forename: Navdeep
authors.initials: N
authors.lastname: Jaitly
authors.forename: Kong
authors.initials: K
authors.lastname: Nguyen
authors.forename: Matthieu
authors.initials: M
authors.lastname: Schapira
authors.forename: Ryan
authors.initials: R
authors.lastname: Lilien
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D002626:Chemistry, Pharmaceutical / Q000379:methods* / Q000706:statistics & numerical data; D057225:Data Mining; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods* / Q000706:statistics & numerical data; D008024:Ligands; D008958:Models, Molecular; D015233:Models, Statistical; D056510:Position-Specific Scoring Matrices; D011485:Protein Binding; D011506:Proteins / Q000032:analysis* / Q000737:chemistry
chemical_list: D008024:Ligands; D011506:Proteins