Evidence map›Paper›PMID 32808340›Full record

ArticleProtein science : a publication of the Protein Society2021

The AutoDock suite at 30.

David S Goodsell, Michel F Sanner, Arthur J Olson, Stefano Forli

Abstract read
In one paragraph

Article in Protein science : a publication of the Protein Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
81citing papers in PubMed, 2 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

81 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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21 more citing papers are in PubMed but not listed here.

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.

David S GoodsellDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.ORCID 0000-0002-5932-2130
Michel F SannerDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.ORCID 0000-0001-9342-327X
Arthur J OlsonDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.ORCID 0000-0003-0558-4618
Stefano ForliDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, California, USA.ORCID 0000-0002-5964-7111

Funding

AutoDock Suite: Next Generation Environment for Drug DesignR01GM069832 · NIGMS · SCRIPPS RESEARCH INSTITUTE, THE · PI FORLI, STEFANO · 2004 to 2025
$10.9M
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 GM069832NIGMS NIH HHS R01 GM096888NIH HHS GM069832NIH HHS GM096888
6 · The paper itself

Abstract

The AutoDock suite provides a comprehensive toolset for computational ligand docking and drug design and development. The suite builds on 30 years of methods development, including empirical free energy force fields, docking engines, methods for site prediction, and interactive tools for visualization and analysis. Specialized tools are available for challenging systems, including covalent inhibitors, peptides, compounds with macrocycles, systems where ordered hydration plays a key role, and systems with substantial receptor flexibility. All methods in the AutoDock suite are freely available for use and reuse, which has engendered the continued growth of a diverse community of primary users and third-party developers.

Indexed as

Drug DesignMolecular Docking SimulationSoftwarePeptidesProteinsPeptidesProteinsactive site predictionAutoDockcomputational dockingcomputer-aided drug designforce fieldpeptide-docking

Identifiers

PMID32808340
PMCPMC7737764

What OpenQuestion holds

Textmetadata
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.