Evidence map›Paper›PMID 42568008›Full record

ArticleMolecular diversity2026

Machine learning-guided QSAR screening of fluconazole analogs and FDA-approved drugs against Candida albicans, with docking, molecular dynamics, and ADMET analysis.

Hamza Age Daudo, Emmanuel Silva Marinho, Victor Moreira de Oliveira, Márcia Machado Marinho, Ribeiro Vasco Ribeiro

Abstract read
PubMed Publisher
In one paragraph

Article in Molecular diversity, 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

5 authors.

Hamza Age DaudoLúrio University, Nampula, Mozambique. tarmamadehamzaagedaudo@gmail.com.ORCID http://orcid.org/0009-0003-2664-8550
Emmanuel Silva MarinhoState University of Ceará, Fortaleza, CE, 60714-903, Brazil.ORCID https://orcid.org/0000-0002-4774-8775
Victor Moreira de OliveiraLaboratory of Bioprospecting and Monitoring of Natural Resources (LBMRN), State University of Ceará (UECE), Fortaleza, CE, 60714-903, Brazil.ORCID https://orcid.org/0000-0002-9261-5656
Márcia Machado MarinhoState University of Ceará, Fortaleza, CE, 60714-903, Brazil.ORCID https://orcid.org/0000-0002-7640-2220
Ribeiro Vasco RibeiroLúrio University, Nampula, Mozambique.ORCID https://orcid.org/0000-0002-3898-1735

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Invasive candidiasis caused by Candida albicans is a critical-priority fungal disease associated with high mortality, and the increasing resistance to fluconazole underscores the urgent need for new antifungal agents. In this study, a machine learning (ML)-guided quantitative structure-activity relationship (QSAR) workflow was developed to prioritize fluconazole analogs and FDA-approved drugs with predicted antifungal activity against Candida, followed by a structure-based exploration of a cell wall target. Bioactivity (IC

Indexed as

ADMETCandida albicansMachine learningMolecular dockingMolecular dynamicsQSAR

Identifiers

PMID42568008

What OpenQuestion holds

Textmetadata
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.