Cart
Research Article
Virtual Screening Using Protein−Ligand Docking: Avoiding Artificial Enrichment
Full Text HTML
Hi-Res PDFPurchase the full-text
- PDF/HTML,
figures/images,
references and tables,
(where available)
Section:Abstract
This study addresses a number of topical issues around the use of protein−ligand docking in virtual screening. We show that, for the validation of such methods, it is key to use focused libraries (containing compounds with one-dimensional properties, similar to the actives), rather than “random” or “drug-like” libraries to test the actives against. We also show that, to obtain good enrichments, the docking program needs to produce reliable binding modes. We demonstrate how pharmacophores can be used to guide the dockings and improve enrichments, and we compare the performance of three consensus-ranking protocols against ranking based on individual scoring functions. Finally, we show that protein−ligand docking can be an effective aid in the screening for weak, fragment-like binders, which has rapidly become a popular strategy for hit identification. All results presented are based on carefully constructed virtual screening experiments against four targets, using the protein−ligand docking program GOLD.
Citing Articles
Citation data is made available by participants in CrossRef's Cited-by Linking service. For a more comprehensive list of citations to this article, users are encouraged to perform a search in SciFinder.
This article has been cited by 73 ACS Journal articles (5 most recent appear below).
Ligand and Decoy Sets for Docking to G Protein-Coupled Receptors
Edgar A. Gatica and Claudio N. CavasottoJournal of Chemical Information and Modeling2012 52 (1), 1-6Ligand and Decoy Sets for Docking to G Protein-Coupled Receptors
Edgar A. Gatica and Claudio N. CavasottoJournal of Chemical Information and Modeling2012 52 (1), 1-6We compiled a G protein-coupled receptor (GPCR) ligand library (GLL) for 147 targets, selecting for each ligand 39 decoy molecules, collected in the GPCR Decoy Database (GDD). Decoys were chosen ensuring a ligand–decoy similarity of six physical ...
DEKOIS: Demanding Evaluation Kits for Objective in Silico Screening — A Versatile Tool for Benchmarking Docking Programs and Scoring Functions
Simon M. Vogel, Matthias R. Bauer, and Frank M. BoecklerJournal of Chemical Information and Modeling2011 51 (10), 2650-2665DEKOIS: Demanding Evaluation Kits for Objective in Silico Screening — A Versatile Tool for Benchmarking Docking Programs and Scoring Functions
Simon M. Vogel, Matthias R. Bauer, and Frank M. BoecklerJournal of Chemical Information and Modeling2011 51 (10), 2650-2665For widely applied in silico screening techniques success depends on the rational selection of an appropriate method. We herein present a fast, versatile, and robust method to construct demanding evaluation kits for objective in silico screening (DEKOIS). ...
Evaluation of Several Two-Step Scoring Functions Based on Linear Interaction Energy, Effective Ligand Size, and Empirical Pair Potentials for Prediction of Protein–Ligand Binding Geometry and Free Energy
Obaidur Rahaman, Trilce P. Estrada, Douglas J. Doren, Michela Taufer, Charles L. Brooks, III, and Roger S. ArmenJournal of Chemical Information and Modeling2011 51 (9), 2047-2065Evaluation of Several Two-Step Scoring Functions Based on Linear Interaction Energy, Effective Ligand Size, and Empirical Pair Potentials for Prediction of Protein–Ligand Binding Geometry and Free Energy
Obaidur Rahaman, Trilce P. Estrada, Douglas J. Doren, Michela Taufer, Charles L. Brooks, III, and Roger S. ArmenJournal of Chemical Information and Modeling2011 51 (9), 2047-2065The performances of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for “step 2 discrimination” were proposed and compared to our ...
CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions
Richard D. Smith, James B. Dunbar, Jr., Peter Man-Un Ung, Emilio X. Esposito, Chao-Yie Yang, Shaomeng Wang, and Heather A. CarlsonJournal of Chemical Information and Modeling2011 51 (9), 2115-2131CSAR Benchmark Exercise of 2010: Combined Evaluation Across All Submitted Scoring Functions
Richard D. Smith, James B. Dunbar, Jr., Peter Man-Un Ung, Emilio X. Esposito, Chao-Yie Yang, Shaomeng Wang, and Heather A. CarlsonJournal of Chemical Information and Modeling2011 51 (9), 2115-2131As part of the Community Structure-Activity Resource (CSAR) center, a set of 343 high-quality, protein–ligand crystal structures were assembled with experimentally determined Kd or Ki information from the literature. We encouraged the community to score ...
Discovery of Novel Pim-1 Kinase Inhibitors by a Hierarchical Multistage Virtual Screening Approach Based on SVM Model, Pharmacophore, and Molecular Docking
Ji-Xia Ren, Lin-Li Li, Ren-Lin Zheng, Huan-Zhang Xie, Zhi-Xing Cao, Shan Feng, You-Li Pan, Xin Chen, Yu-Quan Wei, and Sheng-Yong YangJournal of Chemical Information and Modeling2011 Article ASAPDiscovery of Novel Pim-1 Kinase Inhibitors by a Hierarchical Multistage Virtual Screening Approach Based on SVM Model, Pharmacophore, and Molecular Docking
Ji-Xia Ren, Lin-Li Li, Ren-Lin Zheng, Huan-Zhang Xie, Zhi-Xing Cao, Shan Feng, You-Li Pan, Xin Chen, Yu-Quan Wei, and Sheng-Yong YangJournal of Chemical Information and Modeling2011 Article ASAPIn this investigation, we describe the discovery of novel potent Pim-1 inhibitors by employing a proposed hierarchical multistage virtual screening (VS) approach, which is based on support vector machine-based (SVM-based VS or SB-VS), pharmacophore-based ...
Tools
-
Add to Favorites
-
Download Citation
-
Email a Colleague -
Permalink
Order Reprints
Rights & Permissions
Citation Alerts
History
- Published In Issue May 24, 2004
- Received December 10, 2003
ACS
Network
Facebook
Tweet This
CiteULike
Newsvine
Digg This
Delicious
“Hidden” Impact on Virtual Screening Results
