Evidence map›Paper›PMID 23344039›Full record

ArticleInternational journal of molecular sciences2013

A molecular-modeling toolbox aimed at bridging the gap between medicinal chemistry and computational sciences.

Sameh Eid, Adam Zalewski, Martin Smieško, Beat Ernst, Angelo Vedani

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Sameh EidDepartment of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland. angelo.vedani@unibas.ch.
Adam Zalewski
Martin Smieško
Beat Ernst
Angelo Vedani

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the current era of high-throughput drug discovery and development, molecular modeling has become an indispensable tool for identifying, optimizing and prioritizing small-molecule drug candidates. The required background in computational chemistry and the knowledge of how to handle the complex underlying protocols, however, might keep medicinal chemists from routinely using in silico technologies. Our objective is to encourage those researchers to exploit existing modeling technologies more frequently through easy-to-use graphical user interfaces. In this account, we present two innovative tools (which we are prepared to share with academic institutions) facilitating computational tasks commonly utilized in drug discovery and development: (1) the VirtualDesignLab estimates the binding affinity of small molecules by simulating and quantifying their binding to the three-dimensional structure of a target protein; and (2) the MD Client launches molecular dynamics simulations aimed at exploring the time-dependent stability of ligand-protein complexes and provides residue-based interaction energies. This allows medicinal chemists to identify sites of potential improvement in their candidate molecule. As a case study, we present the application of our tools towards the design of novel antagonists for the FimH adhesin.

Indexed as

Models, MolecularAdhesins, Escherichia coliChemistry, PharmaceuticalComputational BiologyComputer SimulationDrug DesignDrug DiscoveryFimbriae ProteinsHumansMolecular Dynamics SimulationMolecular StructureProtein BindingProteinsProtein Structure, TertiaryReproducibility of ResultsSmall Molecule LibrariesAdhesins, Escherichia coliFimbriae ProteinsfimH protein, E coliProteinsSmall Molecule Libraries

Identifiers

PMID23344039
PMCPMC3565289

What OpenQuestion holds

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LicenceCC BY
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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.