Evidence map›Paper›PMID 39900949›Full record

ArticleScientific reports2025

Using computer modeling to find new LRRK2 inhibitors for parkinson's disease.

María C García, Sebastián A Cuesta, José R Mora, Jose L Paz, Yovani Marrero-Ponce, Frank Alexis, Edgar A Márquez

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. 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

7 authors.

María C GarcíaDepartamento de Ingeniería Química, Diego de Robles y Vía Interoceánica, Universidad San Francisco de Quito, 170901, Quito, Ecuador.ORCID 0009-0009-5283-5337
Sebastián A CuestaDepartamento de Ingeniería Química, Diego de Robles y Vía Interoceánica, Universidad San Francisco de Quito, 170901, Quito, Ecuador.ORCID 0000-0002-8035-6220
José R MoraDepartamento de Ingeniería Química, Diego de Robles y Vía Interoceánica, Universidad San Francisco de Quito, 170901, Quito, Ecuador. jrmora@usfq.edu.ec.ORCID 0000-0001-6128-9504
Jose L PazDepartamento Académico de Química Inorgánica, Facultad de Química e Ingeniería Química, Universidad Nacional Mayor de San Marcos, Lima, Perú.ORCID 0000-0002-6177-7919
Yovani Marrero-PonceGrupo de Medicina Molecular y Traslacional (MeM&T), Universidad San Francisco de Quito, Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA), Av. Interoceánica Km 12 1/2 y Av. Florencia, 17, 1200-841, Quito, Ecuador.ORCID 0000-0003-2721-1142
Frank AlexisDepartamento de Ingeniería Química, Diego de Robles y Vía Interoceánica, Universidad San Francisco de Quito, 170901, Quito, Ecuador.
Edgar A MárquezGrupo de Investigaciones en Química y Biología, Departamento de Química y Biología, Facultad de Ciencias Básicas, Universidad del Norte, Carrera 51B, Km 5, vía Puerto Colombia, Barranquilla, 081007, Colombia. ebrazon@uninorte.edu.co.ORCID 0000-0002-7503-1528

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease (PD) is a complex neurodegenerative disorder that affects multiple neurotransmitters, and its exact cause is still unknown. Developing new drugs for PD is a lengthy and expensive process, making it difficult to find new treatments. This study aims to create a detailed dataset to build strong predictive models with various machine learning algorithms. An ensemble modeling approach was employed to screen the DrugBank database, aiming to repurpose approved medications as potential treatments for Parkinson's disease (PD). The dataset was constructed using pIC50 values of various compounds targeting the inhibition of leucine-rich repeat kinase 2 (LRRK2). The best ensemble model showed exceptional predictive performance, with five-fold cross-validation and external validation metrics exceeding 0.8 (Q

Indexed as

Antiparkinson AgentsLeucine-Rich Repeat Serine-Threonine Protein Kinase-2Parkinson DiseaseProtein Kinase InhibitorsComputer SimulationHumansMachine LearningMolecular Docking SimulationMolecular Dynamics SimulationAntiparkinson AgentsLeucine-Rich Repeat Serine-Threonine Protein Kinase-2LRRK2 protein, humanProtein Kinase InhibitorsDrugBankMolecular dockingMolecular dynamicsParkinson’s diseaseVirtual screening

Identifiers

PMID39900949
PMCPMC11790940

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.