Evidence map›Paper›PMID 41752016›Full record

ArticleInternational journal of molecular sciences2026

Hybrid Computational Framework Integrating Ensemble Learning, Molecular Docking, and Dynamics for Predicting Antimalarial Efficacy of Malaria Box Compounds.

Martín Moreno, Sebastián A Cuesta, José R Mora, Edgar A Márquez Brazon, José L Paz, Guillermin Agüero-Chapin, Noel Pérez-Pérez, César R García-Jacas

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Martín MorenoGrupo de Química Computacional y Teórica (QCT-USFQ), Departamento de Ingeniería Química, Universidad San Francisco de Quito USFQ, Diego de Robles y Vía Interoceánica, Quito 170901, Ecuador.ORCID 0000-0001-8189-148X
Sebastián A CuestaGrupo de Química Computacional y Teórica (QCT-USFQ), Departamento de Ingeniería Química, Universidad San Francisco de Quito USFQ, Diego de Robles y Vía Interoceánica, Quito 170901, Ecuador.
José R MoraGrupo de Química Computacional y Teórica (QCT-USFQ), Departamento de Ingeniería Química, Universidad San Francisco de Quito USFQ, Diego de Robles y Vía Interoceánica, Quito 170901, Ecuador.ORCID 0000-0001-6128-9504
Edgar A Márquez BrazonGrupo 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.ORCID 0000-0002-7503-1528
José L PazDepartamento Académico de Química Inorgánica, Facultad de Química e Ingeniería Química, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru.ORCID 0000-0002-6177-7919
Guillermin Agüero-ChapinCIIMAR/CIMAR LA, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n, 4450-208 Matosinhos, Portugal.ORCID 0000-0002-9908-2418
Noel Pérez-PérezColegio de Ciencias e Ingenierías "El Politécnico", Universidad San Francisco de Quito USFQ, Quito 170157, Ecuador.ORCID 0000-0003-3166-745X
César R García-JacasInvestigador por México, Secretaría de Ciencia, Humanidades, Tecnología e Innovación (Secihti), Ciudad de México 03940, Mexico.ORCID 0000-0002-3962-7658

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of drug-resistant strains of Plasmodium falciparum continues to challenge global malaria control efforts, underscoring the urgent need for novel therapeutic strategies. In this study, we present an integrative computational framework that combines ensemble machine learning, molecular docking, and molecular dynamics simulations to predict and characterize the antimalarial activity of compounds from the Malaria Box database. Initially, topographical and quantum mechanical descriptors were used to construct regression models for predicting pEC

Indexed as

AntimalarialsMachine LearningMalaria, FalciparumMolecular Docking SimulationMolecular Dynamics SimulationPlasmodium falciparumEnsemble LearningHumansAntimalarialsclassificationdrug designensemblemachine learningmalariamolecular dynamicspredictive modelsregression

Identifiers

PMID41752016
PMCPMC12940989

What Socratic 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 Socratic graph.