title: Structure-based virtual screening for drug discovery: a problem-centric review.
abstract: Structure-based virtual screening (SBVS) has been widely applied in early-stage drug discovery. From a problem-centric perspective, we reviewed the recent advances and applications in SBVS with a special focus on docking-based virtual screening. We emphasized the researchers' practical efforts in real projects by understanding the ligand-target binding interactions as a premise. We also highlighted the recent progress in developing target-biased scoring functions by optimizing current generic scoring functions toward certain target classes, as well as in developing novel ones by means of machine learning techniques.
authors.affiliation: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA.
authors.forename: Tiejun
authors.initials: T
authors.lastname: Cheng
authors.forename: Qingliang
authors.initials: Q
authors.lastname: Li
authors.forename: Zhigang
authors.initials: Z
authors.lastname: Zhou
authors.forename: Yanli
authors.initials: Y
authors.lastname: Wang
authors.forename: Stephen H
authors.initials: SH
authors.lastname: Bryant
mesh_terms: D017076:Computer-Aided Design; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods*; D004345:Drug Industry / Q000379:methods; D006801:Humans; D008024:Ligands; D011485:Protein Binding; D013329:Structure-Activity Relationship
chemical_list: D008024:Ligands