Evidence map›Paper›PMID 26501243›Full record

ReviewMolecules (Basel, Switzerland)2015

Charting a Path to Success in Virtual Screening.

Stefano Forli

Abstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Fine tuning for success in structure-based virtual screening.Journal of computer-aided molecular design · 2021
    Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Stefano ForliMolecular Graphics Laboratory, Department of Integrative Structural and Computational Biology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA. forli@scripps.edu.

Funding

AutoDock Suite: Next Generation Environment for Drug DesignR01GM069832 · NIGMS · SCRIPPS RESEARCH INSTITUTE, THE · PI FORLI, STEFANO · 2004 to 2025
$10.9M
NIGMS NIH HHS R01 GM069832NIGMS NIH HHS R01-GM069832
6 · The paper itself

Abstract

Docking is commonly applied to drug design efforts, especially high-throughput virtual screenings of small molecules, to identify new compounds that bind to a given target. Despite great advances and successful applications in recent years, a number of issues remain unsolved. Most of the challenges and problems faced when running docking experiments are independent of the specific software used, and can be ascribed to either improper input preparation or to the simplified approaches applied to achieve high-throughput speed. Being aware of approximations and limitations of such methods is essential to prevent errors, deal with misleading results, and increase the success rate of virtual screening campaigns. In this review, best practices and most common issues of docking and virtual screening will be discussed, covering the journey from the design of the virtual experiment to the hit identification.

Indexed as

Molecular Docking SimulationProtonsSoftwareBinding SitesCrystallography, X-RayDrug DesignHigh-Throughput Screening AssaysHumansLigandsProtein BindingProteinsSmall Molecule LibrariesStatic ElectricityStereoisomerismStructure-Activity RelationshipUser-Computer InterfaceLigandsProteinsProtonsSmall Molecule Librariesdockingdrug designmolecular modelingvirtual screening

Identifiers

PMID26501243
PMCPMC4630810

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.