Evidence map›Paper›PMID 37529284›Full record

ArticleQRB discovery2022

Performance evaluation of flexible macrocycle docking in AutoDock.

Matthew Holcomb, Diogo Santos-Martins, Andreas F Tillack, Stefano Forli

Abstract read
In one paragraph

Article in QRB discovery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Benchmarking Docking Protocols on Predicting Alternative Binding Modes.Journal of chemical information and modeling · 2026
    Article
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  4. Review
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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.

Matthew HolcombDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.ORCID https://orcid.org/0000-0002-8409-4344
Diogo Santos-MartinsDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.ORCID https://orcid.org/0000-0003-4622-3747
Andreas F TillackDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.ORCID https://orcid.org/0000-0002-1832-3030
Stefano ForliDepartment of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA.ORCID https://orcid.org/0000-0002-5964-7111

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Macrocycles represent an important class of ligands, both in natural products and designed drugs. In drug design, macrocyclizations can impart specific ligand conformations and contribute to passive permeation by encouraging intramolecular H-bonds. AutoDock-GPU and Vina can model macrocyclic ligands flexibly, without requiring the enumeration of macrocyclic conformers before docking. Here, we characterize the performance of the method for handling macrocyclic compounds, which is implemented and the default behaviour for ligand preparation with our ligand preparation pipeline, Meeko. A pseudoatom is used to encode bond geometry and produce an anisotropic closure force for macrocyclic rings. This method is evaluated on a diverse set of small molecule and peptide macrocycles, ranging from 7- to 33-membered rings, showing little accuracy loss compared to rigid redocking of the X-ray macrocycle conformers. This suggests that for conformationally flexible macrocycles with unknown binding modes, this method can be effectively used to predict the macrocycle conformation.

Indexed as

autodockdockingmacrocycles

Identifiers

PMID37529284
PMCPMC10392634

What OpenQuestion holds

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LicenceCC BY
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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.