Multivariate assessment of virtual screening experiments

Abstract

Discovering molecules with a desired biological function is one of the great challenges in drug research. To discover new lead molecules, virtual screens (VS) are often conducted, in which databases of molecules are screened for potential binders to a specific protein, using molecular docking. The choice of docking software and parameter settings within the software can significantly influence the outcome of a VS. In this study, we have applied chemometric methods such as design of experiments, principal component analysis and partial least-square projections to latent structure (PLS) to simulated VS experiments to find and compare suitable conditions for performing VS against six protein targets selected from the DUD databases. The docking parameters in FRED, and scoring functions in both FRED and GOLD docking software, were varied according to a statistical experimental design and a PLS model was calculated to correlate the experimental setup to the VS outcome. The study revealed that the choice of scoring function has the greatest influence on VS outcome, and that other parameters have varying influence, depending on the protein target. We also found that substantial bias can be introduced by the lack of variation of molecular properties in the databases used in the screening. Our results provide indications that docking experiments could be tailored to the protein target in order to obtain satisfactory VS results.

Discovery of Ligands for ADP-Ribosyltransferases via Docking-Based Virtual Screening

“…Five of the clusters contained lone compounds, all identified by our docking score resemblance approach. It has been shown that even quite similarly operating scoring functions favor different ligands as top candidates in virtual screening, , which would imply that considering several scoring functions could also increase the diversity among the top candidates, as seen in our results.…”

Targeting Acetylcholinesterase: Identification of Chemical Leads by High Throughput Screening, Structure Determination and Molecular Modeling

“…Most of the published structure-based virtual screens to identify new AChE inhibitors have been based on simulations in which enrichments of known inhibitors have been monitored [31] – [37] . In general, these studies have found poor, or no enrichments of compounds binding to AChE [32] – [36] , illustrating the challenges associated with docking of AChE inhibitors.…”

Evaluating the Predictivity of Virtual Screening for Abl Kinase Inhibitors to Hinder Drug Resistance

“…The DUD set was chosen to best match ABL1 inhibitor-like ligands, and the Glide set is a universal decoy set. The DUD decoy set has been previously used for enrichment studies 28 . The DUD data set has been shown to include analog bias in the active ligand set.…”

“…Directory of Useful Decoys decoy set has been previously used for enrichment studies 28 . Using the Glide universal decoys, only 14.4% of decoys were predicted as hits.…”