Screening Explorer–An Interactive Tool for the Analysis of Screening Results

Screening Explorer is a web-based application that allows for an intuitive evaluation of the results of screening experiments using complementary metrics in the field. The usual evaluation of screening results implies the separate generation and apprehension of the ROC, predictiveness, and enrichment curves and their global metrics. Similarly, partial metrics need to be calculated repeatedly for different fractions of a data set and there exists no handy tool that allows reading partial metrics simultaneously on different charts. For a deeper understanding of the results of screening experiments, we rendered their analysis straightforward by linking these metrics interactively in an interactive usable web-based application. We also implemented simple consensus scoring methods based on scores normalization, standardization (<i>z</i>-scores), and compounds ranking to evaluate the enrichments that can be expected through methods combination. Two demonstration data sets allow the users to easily apprehend the functions of this tool that can be applied to the analysis of virtual and experimental screening results. Screening Explorer is freely accessible at http://stats.drugdesign.fr

Multiple Structures for Virtual Ligand Screening: Defining Binding Site Properties-Based Criteria to Optimize the Selection of the Query

Structure based virtual ligand screening (SBVLS) methods are widely used in drug discovery programs. When several structures of the target are available, protocols based either on single structure docking or on ensemble docking can be used. The performance of the methods depends on the structure(s) used as a reference, whose choice requires retrospective enrichment studies on benchmarking databases which consume additional resources. In the present study, we have identified several trends in the properties of the binding sites of the structures that led to the optimal performance in retrospective SBVLS tests whatever the docking program used (Surflex-dock or ICM). By assessing their hydrophobicity and comparing their volume and opening, we show that the selection of optimal structures should be possible with no requirement of prior retrospective enrichment studies. If the mean binding site volume is lower than 350 A³, the structure with the smaller volume should be preferred. In the other cases, the structure with the largest binding site should be preferred. These optimal structures may be either selected for a single structure docking strategy or an ensemble docking strategy. When constructing an ensemble, the opening of the site might be an interesting criterion additionaly to its volume as the most closed structures should not be preferred in the large systems. These “binding site properties-based” guidelines could be helpful to optimize future prospective drug discovery protocols when several structures of the target are available

Multiple Structures for Virtual Ligand Screening: Defining Binding Site Properties-Based Criteria to Optimize the Selection of the Query

Structure based virtual ligand screening (SBVLS) methods are widely used in drug discovery programs. When several structures of the target are available, protocols based either on single structure docking or on ensemble docking can be used. The performance of the methods depends on the structure(s) used as a reference, whose choice requires retrospective enrichment studies on benchmarking databases which consume additional resources. In the present study, we have identified several trends in the properties of the binding sites of the structures that led to the optimal performance in retrospective SBVLS tests whatever the docking program used (Surflex-dock or ICM). By assessing their hydrophobicity and comparing their volume and opening, we show that the selection of optimal structures should be possible with no requirement of prior retrospective enrichment studies. If the mean binding site volume is lower than 350 A³, the structure with the smaller volume should be preferred. In the other cases, the structure with the largest binding site should be preferred. These optimal structures may be either selected for a single structure docking strategy or an ensemble docking strategy. When constructing an ensemble, the opening of the site might be an interesting criterion additionaly to its volume as the most closed structures should not be preferred in the large systems. These “binding site properties-based” guidelines could be helpful to optimize future prospective drug discovery protocols when several structures of the target are available

Multiple Structures for Virtual Ligand Screening: Defining Binding Site Properties-Based Criteria to Optimize the Selection of the Query

Structure based virtual ligand screening (SBVLS) methods are widely used in drug discovery programs. When several structures of the target are available, protocols based either on single structure docking or on ensemble docking can be used. The performance of the methods depends on the structure(s) used as a reference, whose choice requires retrospective enrichment studies on benchmarking databases which consume additional resources. In the present study, we have identified several trends in the properties of the binding sites of the structures that led to the optimal performance in retrospective SBVLS tests whatever the docking program used (Surflex-dock or ICM). By assessing their hydrophobicity and comparing their volume and opening, we show that the selection of optimal structures should be possible with no requirement of prior retrospective enrichment studies. If the mean binding site volume is lower than 350 A³, the structure with the smaller volume should be preferred. In the other cases, the structure with the largest binding site should be preferred. These optimal structures may be either selected for a single structure docking strategy or an ensemble docking strategy. When constructing an ensemble, the opening of the site might be an interesting criterion additionaly to its volume as the most closed structures should not be preferred in the large systems. These “binding site properties-based” guidelines could be helpful to optimize future prospective drug discovery protocols when several structures of the target are available

Multiple Structures for Virtual Ligand Screening: Defining Binding Site Properties-Based Criteria to Optimize the Selection of the Query

Structure based virtual ligand screening (SBVLS) methods are widely used in drug discovery programs. When several structures of the target are available, protocols based either on single structure docking or on ensemble docking can be used. The performance of the methods depends on the structure(s) used as a reference, whose choice requires retrospective enrichment studies on benchmarking databases which consume additional resources. In the present study, we have identified several trends in the properties of the binding sites of the structures that led to the optimal performance in retrospective SBVLS tests whatever the docking program used (Surflex-dock or ICM). By assessing their hydrophobicity and comparing their volume and opening, we show that the selection of optimal structures should be possible with no requirement of prior retrospective enrichment studies. If the mean binding site volume is lower than 350 A³, the structure with the smaller volume should be preferred. In the other cases, the structure with the largest binding site should be preferred. These optimal structures may be either selected for a single structure docking strategy or an ensemble docking strategy. When constructing an ensemble, the opening of the site might be an interesting criterion additionaly to its volume as the most closed structures should not be preferred in the large systems. These “binding site properties-based” guidelines could be helpful to optimize future prospective drug discovery protocols when several structures of the target are available

MOESM2 of Predictiveness curves in virtual screening

Additional file 2: Table S2. Summary of the partial metrics at 2 % and 5 % of the ordered dataset for virtual screens performed using ICM

MOESM3 of Predictiveness curves in virtual screening

Additional file 3: Table S3. Summary of the partial metrics at 2 % and 5 % of the ordered dataset for virtual screens performed using Autodock Vina

MOESM1 of Predictiveness curves in virtual screening

Additional file 1: Table S1. Summary of the partial metrics at 2 % and 5 % of the ordered dataset for virtual screens performed using Surflex-dock

NRLiSt BDB, the Manually Curated Nuclear Receptors Ligands and Structures Benchmarking Database

Nuclear receptors (NRs) constitute an important class of drug targets. We created the most exhaustive NR-focused benchmarking database to date, the NRLiSt BDB (NRs ligands and structures benchmarking database). The 9905 compounds and 339 structures of the NRLiSt BDB are ready for structure-based and ligand-based virtual screening. In the present study, we detail the protocol used to generate the NRLiSt BDB and its features. We also give some examples of the errors that we found in ChEMBL that convinced us to manually review all original papers. Since extensive and manually curated experimental data about NR ligands and structures are provided in the NRLiSt BDB, it should become a powerful tool to assess the performance of virtual screening methods on NRs, to assist the understanding of NR’s function and modulation, and to support the discovery of new drugs targeting NRs. NRLiSt BDB is freely available online at http://nrlist.drugdesign.fr

Statistical significance of our associations.

<p>Histogram of the number of SNPs that pass the significance criterion for this study using phenotype and SNP randomisations. These results provide us with a way to estimate the sensitivity of our study (diamond): it would be extremely unlikely for our eight independent findings to arise by chance alone (<i>p</i> = 0.001).</p