Literature DB >> 16309298

Considerations in compound database preparation--"hidden" impact on virtual screening results.

Andrew J S Knox1, Mary J Meegan, Giorgio Carta, David G Lloyd.   

Abstract

Structure-based virtual screening (SBVS) utilizing docking algorithms has become an essential tool in the drug discovery process, and significant progress has been made in successfully applying the technique to a wide range of receptor targets. In silico validation of virtual screening protocols before application to a receptor target using a corporate or commercially available compound collection is key to establishing a successful process. Ultimately, retrieval of a set of active compounds from a database of inactives is required, and the metric of enrichment (E) is habitually used to discern the quality of separation of the two. Numerous reports have addressed the performance of docking algorithms with regard to the quality of binding mode prediction and the issue of postprocessing "hit lists" of docked ligands. However, the impact of ligand database preprocessing has yet to be examined in the context of virtual screening and prioritization of compounds for biological evaluation. We provide an insight into the implications of cheminformatic preprocessing of a validation database of compounds where multiple protonated, tautomeric, stereochemical, and conformational states have been enumerated. Several commonly used methods for the generation of ligand conformations and conformational ensembles are examined, paired with an exhaustive rigid-body algorithm for the docking of different "multimeric" compound representations to the ligand binding site of the human estrogen receptor alpha. Chemgauss, a shapegaussian scoring function with intrinsic chemical knowledge, was combined with PLP as a consensus-scoring scheme to rank output from the docking protocol and enrichment rates calculated for each screen. The overheads of CPU consumption and the effect on relative database size (disk requirement) for each of the protocols employed are considered. Assessment of these parameters indicates that SBVS enrichments are highly dependent on the initial cheminformatic treatment(s) used in database construction. The interplay of SMILES representations, stereochemical information, protonation state enumeration, and ligand conformation ensembles are critical in achieving optimum enrichment rates in such screening.

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Year:  2005        PMID: 16309298     DOI: 10.1021/ci050185z

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  8 in total

1.  Permuting input for more effective sampling of 3D conformer space.

Authors:  Giorgio Carta; Valeria Onnis; Andrew J S Knox; Darren Fayne; David G Lloyd
Journal:  J Comput Aided Mol Des       Date:  2006-07-14       Impact factor: 3.686

Review 2.  Evaluation of the performance of 3D virtual screening protocols: RMSD comparisons, enrichment assessments, and decoy selection--what can we learn from earlier mistakes?

Authors:  Johannes Kirchmair; Patrick Markt; Simona Distinto; Gerhard Wolber; Thierry Langer
Journal:  J Comput Aided Mol Des       Date:  2008-01-15       Impact factor: 3.686

3.  VSDMIP: virtual screening data management on an integrated platform.

Authors:  Rubén Gil-Redondo; Jorge Estrada; Antonio Morreale; Fernando Herranz; Javier Sancho; Angel R Ortiz
Journal:  J Comput Aided Mol Des       Date:  2008-10-22       Impact factor: 3.686

Review 4.  Understanding nuclear receptors using computational methods.

Authors:  Ni Ai; Matthew D Krasowski; William J Welsh; Sean Ekins
Journal:  Drug Discov Today       Date:  2009-03-11       Impact factor: 7.851

Review 5.  The role of protonation states in ligand-receptor recognition and binding.

Authors:  Marharyta Petukh; Shannon Stefl; Emil Alexov
Journal:  Curr Pharm Des       Date:  2013       Impact factor: 3.116

6.  Effects of histidine protonation and rotameric states on virtual screening of M. tuberculosis RmlC.

Authors:  Meekyum Olivia Kim; Sara E Nichols; Yi Wang; J Andrew McCammon
Journal:  J Comput Aided Mol Des       Date:  2013-04-12       Impact factor: 3.686

Review 7.  Benchmarking Data Sets from PubChem BioAssay Data: Current Scenario and Room for Improvement.

Authors:  Viet-Khoa Tran-Nguyen; Didier Rognan
Journal:  Int J Mol Sci       Date:  2020-06-19       Impact factor: 5.923

8.  Ligand scaffold hopping combining 3D maximal substructure search and molecular similarity.

Authors:  Flavien Quintus; Olivier Sperandio; Julien Grynberg; Michel Petitjean; Pierre Tuffery
Journal:  BMC Bioinformatics       Date:  2009-08-11       Impact factor: 3.169

  8 in total

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