title: Getting the most out of PubChem for virtual screening.
abstract: BACKGROUND
  With the emergence of the 'big data' era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge.
  
  
  METHODS
  This article provides an overview of how PubChem's data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery.
  
  
  CONCLUSIONS
  PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.
authors.affiliation: a National Center for Biotechnology Information, National Library of Medicine , National Institutes of Health , Department of Health and Human Services, Bethesda , MD , USA.
authors.forename: Sunghwan
authors.initials: S
authors.lastname: Kim
mesh_terms: D000818:Animals; D062126:Databases, Chemical; D015195:Drug Design; D055808:Drug Discovery / Q000379:methods*; D058492:Drug Repositioning; D057166:High-Throughput Screening Assays; D006801:Humans; D008024:Ligands; D058990:Molecular Targeted Therapy
chemical_list: D008024:Ligands
keywords: PubChem; biological assay; cheminformatics; computational toxicology; computer-aided drug discovery; data mining; database; polypharmacology; quantitative structure-activity relationship (QSAR); virtual screening