Evidence map›Paper›PMID 31323745›Full record

ArticleMolecules (Basel, Switzerland)2019

Local Interaction Density (LID), a Fast and Efficient Tool to Prioritize Docking Poses.

Célien Jacquemard, Viet-Khoa Tran-Nguyen, Malgorzata N Drwal, Didier Rognan, Esther Kellenberger

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

5 authors.

Célien JacquemardLaboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France.
Viet-Khoa Tran-NguyenLaboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France.
Malgorzata N DrwalLaboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France.
Didier RognanLaboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France.ORCID 0000-0002-0577-641X
Esther KellenbergerLaboratoire D'innovation Thérapeutique, UMR7200, CNRS, Université de Strasbourg, 67400 Illkirch, France. ekellen@unistra.fr.ORCID 0000-0002-9320-4840

Funding

Agence Nationale de la Recherche ANR-10-LABX-0034
6 · The paper itself

Abstract

Ligand docking at a protein site can be improved by prioritizing poses by similarity to validated binding modes found in the crystal structures of ligand/protein complexes. The interactions formed in the predicted model are searched in each of the reference 3D structures, taken individually. We propose to merge the information provided by all references, creating a single representation of all known binding modes. The method is called LID, an acronym for Local Interaction Density. LID was benchmarked in a pose prediction exercise on 19 proteins and 1382 ligands using PLANTS as docking software. It was also tested in a virtual screening challenge on eight proteins, with a dataset of 140,000 compounds from DUD-E and PubChem. LID significantly improved the performance of the docking program in both pose prediction and virtual screening. The gain is comparable to that obtained with a rescoring approach based on the individual comparison of reference binding modes (the GRIM method). Importantly, LID is effective with a small number of references. LID calculation time is negligible compared to the docking time.

Indexed as

Molecular Docking SimulationMolecular Dynamics SimulationAlgorithmsBinding SitesCrystallography, X-RayDrug Evaluation, PreclinicalHumansLigandsMolecular ConformationProtein BindingProteinsReproducibility of ResultsROC CurveLigandsProteinsbenchmarkingprotein ligand interactionscoring

Identifiers

PMID31323745
PMCPMC6681060

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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.