Evidence map›Paper›PMID 40263231›Full record

ArticleMolecular diversity2025

Advancing antimalarial drug discovery: ensemble machine learning models for predicting PfPK6 inhibitor activity.

Maryam Gholami, Mohammad Asadollahi-Baboli

Abstract read
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Article in Molecular diversity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

2 authors.

Maryam GholamiDepartment of Chemistry, Faculty of Science, Babol Noshirvani University of Technology, Babol, 47148-71167, Mazandaran, Iran.
Mohammad Asadollahi-BaboliDepartment of Chemistry, Faculty of Science, Babol Noshirvani University of Technology, Babol, 47148-71167, Mazandaran, Iran. asadollahi@nit.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malaria is a significant global health challenge, causing high morbidity and mortality. The rise of drug resistance highlights the urgent need for new antimalarial agents. This study focuses on predictive modeling of 104 Plasmodium falciparum protein kinase 6 (PfPK6) inhibitors, employing a range of machine learning techniques to develop ensemble regression and classification models. Molecular descriptors were refined using classification and regression trees (CART) to identify the most relevant features. Six machine learning algorithms (Random Forest (RF), Relevance Vector Machine (RVM), Support Vector Machine (SVM), Cubist, Artificial Neural Networks (ANN), and XGBoost) were utilized to construct regression models. The consensus model demonstrated superior predictive performance, achieving R

Indexed as

AntimalarialsDrug DiscoveryMachine LearningPlasmodium falciparumProtein Kinase InhibitorsProtozoan ProteinsAlgorithmsNeural Networks, ComputerSupport Vector MachineAntimalarialsProtein Kinase InhibitorsProtozoan ProteinsClassification and regression trees (CART)Ensemble machine learning techniquesMalaria treatmentPlasmodium falciparum protein kinase 6 (PfPK6)Quantitative structure–activity relationship (QSAR)

Identifiers

PMID40263231

What OpenQuestion holds

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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.