Evidence map›Paper›PMID 40685384›Full record

ArticleScientific reports2025

Advanced QSPR modeling of profens using machine learning and molecular descriptors for NSAID analysis.

W Eltayeb Ahmed, Muhammad Farhan Hanif, Muhammad Kamran Siddiqui, Brima Gegbe

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. 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

4 authors.

W Eltayeb AhmedDepartment of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Muhammad Farhan HanifDepartment of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan.
Muhammad Kamran SiddiquiDepartment of Mathematics, COMSATS University Islamabad, Lahore Campus, Islamabad, Pakistan.
Brima GegbeDepartment of Mathematics and Statistics, Njala University, Freetown, Sierra Leone. bgegbe@njala.edu.sl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we present a predictive model based on artificial neural network (ANN) to evaluate principal physicochemical properties of a set of anti-inflammatory drugs based on chosen topological indices. The molecular descriptors were calculated from molecular structures and employed as the inputs to the ANN model. Normalization of the feature set was carried out before training to maintain convergence and stability of the model. The ANN exhibited excellent predictive ability based on a [Formula: see text] value of 0.94 and a mean squared error (MSE) of 0.0087 on the test set. The chemical structure data used were mainly retrieved from ChemSpider. The method showcases the promise of machine learning models to facilitate better virtual screening and assist in rational drug design by making accurate predictions of properties.

Indexed as

Anti-Inflammatory Agents, Non-SteroidalMachine LearningQuantitative Structure-Activity RelationshipNeural Networks, ComputerAnti-Inflammatory Agents, Non-SteroidalComputational chemistryDrug designGraph theoryMachine learningMolecular modelingNSAIDsPredictive analyticsQSPRStructure-activity relationshipTopological indices

Identifiers

PMID40685384
PMCPMC12277417

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.