Evidence map›Paper›PMID 30544890›Full record

ReviewMolecules (Basel, Switzerland)2018

Solvents to Fragments to Drugs: MD Applications in Drug Design.

Lucas A Defelipe, Juan Pablo Arcon, Carlos P Modenutti, Marcelo A Marti, Adrián G Turjanski, Xavier Barril

Abstract readReview
In one paragraph

Review in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Cosolvent Sites-Based Discovery ofJournal of medicinal chemistry · 2022
    Article
  10. Article
  11. Article
  12. Ten things I `hate' about refinement.Acta crystallographica. Section D, Structural biology · 2021
    Article
  13. Structure-based molecular modeling in SAR analysis and lead optimization.Computational and structural biotechnology journal · 2021
    Review
  14. Biased Docking for Protein-Ligand Pose Prediction.Methods in molecular biology (Clifton, N.J.) · 2021
    Article
  15. Review
  16. Review
  17. Article
  18. Molecular Modeling in Drug Design.Molecules (Basel, Switzerland) · 2019
    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

6 authors.

Lucas A DefelipeDepartamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428, Argentina. ldefelipe@gmail.com.ORCID 0000-0001-7859-7300
Juan Pablo ArconDepartamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428, Argentina. juanarcon@gmail.com.ORCID 0000-0003-3350-1576
Carlos P ModenuttiDepartamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428, Argentina. cpmode@gmail.com.
Marcelo A MartiDepartamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428, Argentina. marti.marcelo@gmail.com.
Adrián G TurjanskiDepartamento de Química Biológica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires 1428, Argentina. adrian@qb.fcen.uba.ar.
Xavier BarrilCatalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain. xbarril@ub.edu.ORCID 0000-0002-0281-1347

Funding

Generalitat de Catalunya 2014 SGR 1189Ministerio de Ciencia, Tecnología e Innovación Productiva PICT 2015-2276Ministerio de Economía y Competitividad SAF2015-68749-R
6 · The paper itself

Abstract

Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.

Indexed as

Drug DesignMolecular Dynamics SimulationDrug DiscoveryLigandsProteinsSolventsLigandsProteinsSolventscosolvent molecular dynamicsdockingdrug designfragment screeningmolecular dynamics

Identifiers

PMID30544890
PMCPMC6321499

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.