Evidence map›Paper›PMID 30718737›Full record

ArticleScientific reports2019

OptiPharm: An evolutionary algorithm to compare shape similarity.

S Puertas-Martín, J L Redondo, P M Ortigosa, H Pérez-Sánchez

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. AutoDock-SS: AutoDock for Multiconformational Ligand-Based Virtual Screening.Journal of chemical information and modeling · 2024
    Article
  3. Article
  4. Article
  5. Review
  6. 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.

S Puertas-MartínSupercomputing - Algorithms Research Group (SAL), University of Almería, Agrifood Campus of International Excellence, ceiA3, Almería, 04120, Spain. savinspm@ual.es.ORCID 0000-0001-8956-1733
J L RedondoSupercomputing - Algorithms Research Group (SAL), University of Almería, Agrifood Campus of International Excellence, ceiA3, Almería, 04120, Spain.
P M OrtigosaSupercomputing - Algorithms Research Group (SAL), University of Almería, Agrifood Campus of International Excellence, ceiA3, Almería, 04120, Spain.
H Pérez-SánchezBioinformatics and High Performance Computing Research Group (BIO-HPC), Universidad Católica de Murcia (UCAM), Murcia, 30107, Spain. hperez@ucam.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Virtual Screening (VS) methods can drastically accelerate global drug discovery processes. Among the most widely used VS approaches, Shape Similarity Methods compare in detail the global shape of a query molecule against a large database of potential drug compounds. Even so, the databases are so enormously large that, in order to save time, the current VS methods are not exhaustive, but they are mainly local optimizers that can easily be entrapped in local optima. It means that they discard promising compounds or yield erroneous signals. In this work, we propose the use of efficient global optimization techniques, as a way to increase the quality of the provided solutions. In particular, we introduce OptiPharm, which is a parameterizable metaheuristic that improves prediction accuracy and offers greater computational performance than WEGA, a Gaussian-based shape similarity method. OptiPharm includes mechanisms to balance between exploration and exploitation to quickly identify regions in the search space with high-quality solutions and avoid wasting time in non-promising areas. OptiPharm is available upon request via email.

Indexed as

AlgorithmsMolecular StructureArea Under CurveDatabases, ChemicalHydrogenModels, MolecularUnited StatesUnited States Food and Drug AdministrationHydrogen

Identifiers

PMID30718737
PMCPMC6361934

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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.