Evidence map›Paper›PMID 40372610›Full record

ArticlePhotochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology2025

Machine learning-based bioactivity prediction of porphyrin derivatives: molecular descriptors, clustering, and model evaluation.

Tugba Muhlise Okyay, Ibrahim Yilmaz, Macit Koldas

Abstract read
PubMed Publisher
In one paragraph

Article in Photochemical & photobiological sciences : Official journal of the European Photochemistry Association and the European Society for Photobiology, 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. 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

3 authors.

Tugba Muhlise OkyayMedical Biochemistry, University of Health Sciences, 34956, Istanbul, Türkiye.ORCID http://orcid.org/0000-0001-8529-0331
Ibrahim YilmazDepartment of Medical Biochemistry, Health Science University Istanbul Haseki Training and Research Hospital, Istanbul, Türkiye.ORCID http://orcid.org/0000-0001-8967-2708
Macit KoldasMedical Biochemistry, University of Health Sciences, 34956, Istanbul, Türkiye. macit.koldas@sbu.edu.tr.ORCID http://orcid.org/0000-0003-4719-0246

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the relationship between molecular structure and bioactivity is crucial for optimizing porphyrin-based therapeutics. By integrating cheminformatics techniques with machine learning models, our work enables the efficient classification of compounds based on their molecular structures and their growth inhibition capabilities (IC

Indexed as

Antineoplastic AgentsMachine LearningPorphyrinsCell ProliferationCluster AnalysisHumansMolecular StructureAntineoplastic AgentsPorphyrinsCheminformaticsIC50Machine learningPhotodynamic therapyPorphyrin

Identifiers

PMID40372610

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.