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.
Highlights
The discovery of innovative leads with potential interaction to specific targets is of central importance to the early-stage drug discovery
There have been a mounting number of success stories reported by use of structure-based virtual screening (SBVS) [4,6], among which docking-based virtual screening (DBVS) is arguably the most widely applied one in practice [7]
We reviewed the recent advances and applications in DBVS from a problem-centric perspective with an emphasis on the integration of available knowledge adopted by researchers in real practice
Summary
The discovery of innovative leads with potential interaction to specific targets is of central importance to the early-stage drug discovery. Each compound in the library is virtually docked into the target binding site through a docking program, which computationally models the ligand–target interaction to achieve an optimal complementarity of steric and physicochemical properties. Gozalbes et al have enriched a kinase-targeted compound library using kinase-specific filters, which were derived from systematic docking and scoring of 123 diverse ligands against three kinases with known crystal structures [28].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
