Abstract
BackgroundStructure-based virtual screening techniques can help to identify new lead structures and complement other screening approaches in drug discovery. Prior to docking, the data (protein crystal structures and ligands) should be prepared with great attention to molecular and chemical details.ResultsUsing a subset of 18 diverse targets from the recently introduced DEKOIS 2.0 benchmark set library, we found differences in the virtual screening performance of two popular docking tools (GOLD and Glide) when employing two different commercial packages (e.g. MOE and Maestro) for preparing input data. We systematically investigated the possible factors that can be responsible for the found differences in selected sets. For the Angiotensin-I-converting enzyme dataset, preparation of the bioactive molecules clearly exerted the highest influence on VS performance compared to preparation of the decoys or the target structure. The major contributing factors were different protonation states, molecular flexibility, and differences in the input conformation (particularly for cyclic moieties) of bioactives. In addition, score normalization strategies eliminated the biased docking scores shown by GOLD (ChemPLP) for the larger bioactives and produced a better performance. Generalizing these normalization strategies on the 18 DEKOIS 2.0 sets, improved the performances for the majority of GOLD (ChemPLP) docking, while it showed detrimental performances for the majority of Glide (SP) docking.ConclusionsIn conclusion, we exemplify herein possible issues particularly during the preparation stage of molecular data and demonstrate to which extent these issues can cause perturbations in the virtual screening performance. We provide insights into what problems can occur and should be avoided, when generating benchmarks to characterize the virtual screening performance. Particularly, careful selection of an appropriate molecular preparation setup for the bioactive set and the use of score normalization for docking with GOLD (ChemPLP) appear to have a great importance for the screening performance. For virtual screening campaigns, we recommend to invest time and effort into including alternative preparation workflows into the generation of the master library, even at the cost of including multiple representations of each molecule.Graphical Using DEKOIS 2.0 benchmark sets in structure-based virtual screening to probe the impact of molecular preparation and score normalization.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-015-0074-6) contains supplementary material, which is available to authorized users.
Highlights
Structure-based virtual screening techniques can help to identify new lead structures and complement other screening approaches in drug discovery
Selection of benchmark sets To probe the impact of diverse docking setups on Virtual screening (VS) performance, suitable and diverse test datasets should be employed
Our recently introduced DEKOIS 2.0 library offers a wide variety of curated high-quality benchmark sets and is well-suited for compiling a selection of evaluation kits [21, 24]
Summary
Structure-based virtual screening techniques can help to identify new lead structures and complement other screening approaches in drug discovery. Virtual screening (VS) is a widely applied method in drug discovery that is used to predict novel bioactives from large chemical libraries [1,2,3,4]. The higher the number of bioactives at the top of the score-ordered list of screened molecules, the better is the respective screening performance. Apart from the selection of a suitable docking tool, the success rate of VS tools depends strongly on various factors, such as the protonation/tautomerization state of the respective protein binding site residues [26] and of the input molecules [27,28,29,30], as well as the force field-minimized input conformation of the respective input molecules [31,32,33,34,35]
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