Virtual Screening Using Protein−Ligand Docking: Avoiding Artificial Enrichment

Citations Over TimeTop 1% of 2004 papers

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.

Related Papers

• → Computational protein–ligand docking and virtual drug screening with the AutoDock suite(2016)2,604 cited
• → Comparison of Shape-Matching and Docking as Virtual Screening Tools(2006)1,004 cited
• → Protein Flexibility in Ligand Docking and Virtual Screening to Protein Kinases(2004)413 cited
• → A cross docking pipeline for improving pose prediction and virtual screening performance(2017)30 cited
• → PLHINT: A knowledge-driven computational approach based on the intermolecular H bond interactions at the protein-ligand interface from docking solutions(2017)14 cited