Evidence map›Paper›PMID 31965404›Full record

ArticleJournal of computer-aided molecular design2020

Conformational analysis of macrocycles: comparing general and specialized methods.

Gustav Olanders, Hiba Alogheli, Peter Brandt, Anders Karlén

Abstract read
In one paragraph

Article in Journal of computer-aided molecular design, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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  12. Benchmark of Generic Shapes for Macrocycles.Journal of chemical information and modeling · 2020
    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.

Gustav OlandersDepartment of Medicinal Chemistry, Uppsala University, BMC, Box 574, 751 23, Uppsala, Sweden.ORCID 0000-0003-4713-8368
Hiba AlogheliDepartment of Medicinal Chemistry, Uppsala University, BMC, Box 574, 751 23, Uppsala, Sweden.
Peter BrandtMedicinal Chemistry, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.ORCID 0000-0002-2885-2016
Anders KarlénDepartment of Medicinal Chemistry, Uppsala University, BMC, Box 574, 751 23, Uppsala, Sweden. anders.karlen@ilk.uu.se.ORCID 0000-0001-9512-6981

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Macrocycles represent an important class of medicinally relevant small molecules due to their interesting biological properties. Therefore, a firm understanding of their conformational preferences is important for drug design. Given the importance of macrocycle-protein modelling in drug discovery, we envisaged that a systematic study of both classical and recent specialized methods would provide guidance for other practitioners within the field. In this study we compare the performance of the general, well established conformational analysis methods Monte Carlo Multiple Minimum (MCMM) and Mixed Torsional/Low-Mode sampling (MTLMOD) with two more recent and specialized macrocycle sampling techniques: MacroModel macrocycle Baseline Search (MD/LLMOD) and Prime macrocycle conformational sampling (PRIME-MCS). Using macrocycles extracted from 44 macrocycle-protein X-ray crystallography complexes, we evaluated each method based on their ability to (i) generate unique conformers, (ii) generate unique macrocycle ring conformations, (iii) identify the global energy minimum, (iv) identify conformers similar to the X-ray ligand conformation after Protein Preparation Wizard treatment (X-ray

Indexed as

Computational ChemistryDrug DiscoveryProtein ConformationCrystallography, X-RayDrug DesignHumansLigandsMolecular Dynamics SimulationProteinsThermodynamicsLigandsProteinsConformational samplingDrug designMacrocycles

Identifiers

PMID31965404
PMCPMC7036058

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Registered trials

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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.