title: A unified, probabilistic framework for structure- and ligand-based virtual screening.
abstract: We present a probabilistic framework for interpreting structure-based virtual screening that returns a quantitative likelihood of observing bioactivity and can be quantitatively combined with ligand-based screening methods to yield a cumulative prediction that consistently outperforms any single screening metric. The approach has been developed and validated on more than 30 different protein targets. Transforming structure-based in silico screening results into robust probabilities of activity enables the general fusion of multiple structure- and ligand-based approaches and returns a quantitative expectation of success that can be used to prioritize (or deprioritize) further discovery activities. This unified probabilistic framework offers a paradigm shift in how docking and scoring results are interpreted, which can enhance early lead-finding efforts by maximizing the value of in silico computational tools.
authors.affiliation: Global Pharmaceutical Research and Development, Abbott Laboratories, 100 Abbott Park Road, Abbott Park, Illinois 60064, United States.
authors.forename: Steven L
authors.initials: SL
authors.lastname: Swann
authors.forename: Scott P
authors.initials: SP
authors.lastname: Brown
authors.forename: Steven W
authors.initials: SW
authors.lastname: Muchmore
authors.forename: Hetal
authors.initials: H
authors.lastname: Patel
authors.forename: Philip
authors.initials: P
authors.lastname: Merta
authors.forename: John
authors.initials: J
authors.lastname: Locklear
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Hajduk
mesh_terms: D016208:Databases, Factual; D008024:Ligands; D008958:Models, Molecular; D015394:Molecular Structure; D011336:Probability; D011506:Proteins / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D008024:Ligands; D011506:Proteins