Machine Learning Techniques for Computational Characterization of Protein-Associated Small Molecules with Therapeutic Relevance.

Jabbar Ali, Muhammad Aqib, Yasir Ali, Mehar Ali Malik

Abstract read

In one paragraph

Article in ACS omega, 2026. 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.

Graph-Theoretical Approach for Predicting Physicochemical Properties of Stiff-Person Syndrome Drugs.ChemistryOpen · 2026
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.

Jabbar AliDepartment of Basic Sciences and Humanities, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), P.O. Box 45200, Main Peshawar Road, Rawalpindi, Pakistan.ORCID https://orcid.org/0009-0000-9310-3276

Muhammad AqibDepartment of Basic Sciences and Humanities, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), P.O. Box 45200, Main Peshawar Road, Rawalpindi, Pakistan.

Yasir AliDepartment of Basic Sciences and Humanities, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), P.O. Box 45200, Main Peshawar Road, Rawalpindi, Pakistan.

Mehar Ali MalikDepartment of Basic Sciences and Humanities, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), P.O. Box 45200, Main Peshawar Road, Rawalpindi, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents a computational QSPR framework for the analysis of protein-associated small molecular graphs by using machine learning under limited sample conditions. Protein related molecules were modeled as molecular graphs, and their structural characteristics were quantified using topological indices, physicochemical properties, and molecular complexity descriptors. To address the high dimensionality relative to the sample size (

Identifiers

PMID42040421

DOI10.1021/acsomega.5c11670

PMCPMC13103855

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LicenceCC BY-NC-ND

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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.