Evidence map›Paper›PMID 31540192›Full record

ReviewInternational journal of molecular sciences2019

Key Topics in Molecular Docking for Drug Design.

Pedro H M Torres, Ana C R Sodero, Paula Jofily, Floriano P Silva-Jr

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 198 papers.

0numbers the graph read from it
0cells of the map it votes in
198citing 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

198 citing papers in PubMed.

  1. Kaempferol activity onPharmaceutical biology · 2026
    Article
  2. Review
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  13. Review
  14. Multidrug-ResistantMicroorganisms · 2026
    Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Therapeutic Antitoxoplasmosis Potential ofJournal of tropical medicine · 2026
    Article

138 more citing papers are in PubMed but not listed here.

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.

Pedro H M TorresDepartment of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK. monteirotorres@gmail.com.ORCID 0000-0002-0945-1495
Ana C R SoderoDepartment of Drugs and Medicines; School of Pharmacy; Federal University of Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil. acrsodero@gmail.com.
Paula JofilyLaboratório de Modelagem e Dinâmica Molecular, Instituto de Biofísica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21949-900, RJ, Brazil. paula.jofily@gmail.com.ORCID 0000-0003-1752-9421
Floriano P Silva-JrLaboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, FIOCRUZ, Rio de Janeiro 21949-900, RJ, Brazil. floriano@ioc.fiocruz.br.ORCID 0000-0003-4560-1291

Funding

Academy of Medical Sciences ITPMZO55Conselho Nacional de Desenvolvimento Científico e Tecnológico 304059/2018-8
6 · The paper itself

Abstract

Molecular docking has been widely employed as a fast and inexpensive technique in the past decades, both in academic and industrial settings. Although this discipline has now had enough time to consolidate, many aspects remain challenging and there is still not a straightforward and accurate route to readily pinpoint true ligands among a set of molecules, nor to identify with precision the correct ligand conformation within the binding pocket of a given target molecule. Nevertheless, new approaches continue to be developed and the volume of published works grows at a rapid pace. In this review, we present an overview of the method and attempt to summarise recent developments regarding four main aspects of molecular docking approaches: (i) the available benchmarking sets, highlighting their advantages and caveats, (ii) the advances in consensus methods, (iii) recent algorithms and applications using fragment-based approaches, and (iv) the use of machine learning algorithms in molecular docking. These recent developments incrementally contribute to an increase in accuracy and are expected, given time, and together with advances in computing power and hardware capability, to eventually accomplish the full potential of this area.

Indexed as

Drug DesignMolecular Docking SimulationMolecular Dynamics SimulationAlgorithmsMachine LearningModels, MolecularStructure-Activity Relationshipbenchmarking setscomputer-aided drug designconsensus methodsfragment-basedmachine learningstructure-based drug design

Identifiers

PMID31540192
PMCPMC6769580

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.