Evidence map›Paper›PMID 31436329›Full record

ArticleJournal of computational chemistry2019

AutoGridFR: Improvements on AutoDock Affinity Maps and Associated Software Tools.

Yuqi Zhang, Stefano Forli, Anna Omelchenko, Michel F Sanner

Abstract read
In one paragraph

Article in Journal of computational chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–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

32 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Locusts adopt IPScience advances · 2025
    Article
  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

4 authors.

Yuqi ZhangIntegrated Computational and Structural Biology, The Scripps Research Institute, La Jolla, California.
Stefano ForliIntegrated Computational and Structural Biology, The Scripps Research Institute, La Jolla, California.
Anna OmelchenkoIntegrated Computational and Structural Biology, The Scripps Research Institute, La Jolla, California.
Michel F SannerIntegrated Computational and Structural Biology, The Scripps Research Institute, La Jolla, California.ORCID 0000-0001-9342-327X

Funding

In-silico prediction of protein-peptide interactions.R01GM096888 · NIGMS · SCRIPPS RESEARCH INSTITUTE, THE · PI SANNER, MICHEL F. · 2011 to 2023
$5.1M
NIGMS NIH HHS R01 GM096888
6 · The paper itself

Abstract

Precomputed affinity maps are used by AutoDock to efficiently describe rigid biomolecules called receptors in automated docking. These maps greatly speed up the docking process and allow users to experiment with the forcefield. Here, we present AutoGridFR (AGFR): a software tool facilitating the calculation of these maps. We describe a new version of the AutoSite algorithm that improves the description of binding pockets automatically detected on receptors, and an algorithm for adding affinity gradients which help search methods optimize solution using fewer evaluations of the scoring functions. AGFR supports the calculation of maps for various advanced docking techniques such as covalent docking, hydrated docking, and docking with flexible receptor sidechains. Maps are stored in a single file along with metadata supporting data provenance, reproducibility, and facilitating their management. Finally, maps can be calculated from the command line or through a modern graphical user interface which also supports their visualization. © 2019 Wiley Periodicals, Inc.

Indexed as

AlgorithmsAutomationMolecular Docking SimulationSoftwareProteinsProteinsaffinity mapscovalent dockingdockingdocking with flexible receptor sidechainsprovenance

Identifiers

PMID31436329
PMCPMC7737998

What OpenQuestion holds

Textmetadata
LicenceTDM
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.