Treffer: On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling.

Title:
On QSPR analysis of pulmonary cancer drugs using python-driven topological modeling.
Authors:
Qin H; Department of Rehabilitation Medicine, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.; Key Laboratory of Research and Development on Clinical Molecular Diagnosis for High-Incidence Diseases of Baise, Baise, Guangxi, China., Hussain M; Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan., Hanif MF; Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan., Siddiqui MK; Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan., Hussain Z; Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan., Fiidow MA; Department of Mathematical Sciences, Faculty of Science, Somali National University, Mogadishu Campus, Somalia. m.fiidow@snu.edu.so.
Source:
Scientific reports [Sci Rep] 2025 Feb 01; Vol. 15 (1), pp. 3965. Date of Electronic Publication: 2025 Feb 01.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
MeSH Terms:
Lung Neoplasms*/drug therapy , Antineoplastic Agents*/chemistry , Antineoplastic Agents*/therapeutic use , Antineoplastic Agents*/pharmacology , Quantitative Structure-Activity Relationship*, Humans ; Software ; Drug Discovery/methods
References:
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Contributed Indexing:
Keywords: Linear regression model; Networks; Pulmonary cancer drugs; Python technique; Topological descriptor
Substance Nomenclature:
0 (Antineoplastic Agents)
Entry Date(s):
Date Created: 20250201 Date Completed: 20250505 Latest Revision: 20250521
Update Code:
20250521
PubMed Central ID:
PMC11787360
DOI:
10.1038/s41598-025-88419-0
PMID:
39893301
Database:
MEDLINE

Weitere Informationen

In this paper, we discussed the role of topological descriptors in the QSPR modeling of pulmonary cancer drugs. Degree-based topological indices were computed using computational methods driven by Python that are mathematical representations of properties of molecules without physical measurement. These descriptors were analyzed through linear regression models using SPSS software to predict significant physicochemical properties like boiling point, flash point, molar refractivity, and polarizability. The results show excellent correlations between the computed indices and the observed properties, except for flash point, which ascertains the dependability of the approach in QSPR analysis. The integration of computational and mathematical chemistry will make it easier to evaluate drugs because it can assure consistent data for preclinical development. The paper also reveals specific indices that are superior to others regarding predictive accuracy, thus giving a basis for refining the models to suit the individual compound. This review sets the pace for establishing methodologies that are efficient in designing new and efficient treatments against cancer since it gives insight into the strengths and limitations of topological modeling. This work marked the transformation in accelerating the math involved in drug discovery to reduce such research costs.
(© 2025. The Author(s).)

Declarations. Competing interests: The authors declare no competing interests.