Treffer: On QSPR analysis of glaucoma drugs using machine learning with XGBoost and regression models.

Title:
On QSPR analysis of glaucoma drugs using machine learning with XGBoost and regression models.
Authors:
Huang L; Department of Rehabilitation Medicine, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China. Electronic address: 14736050709@163.com., Alhulwah KH; Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11623, Saudi Arabia. Electronic address: khalhulwah@imamu.edu.sa., Hanif MF; Department of Mathematics and Statistics, The University of Lahore, Lahore Campus, Pakistan. Electronic address: farhanlums@gmail.com., Siddiqui MK; Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan. Electronic address: kamransiddiqui75@gmail.com., Ikram AS; Department of Mathematics and Statistics, The University of Lahore, Lahore Campus, Pakistan. Electronic address: ambar.ikram@math.uol.edu.pk.
Source:
Computers in biology and medicine [Comput Biol Med] 2025 Mar; Vol. 187, pp. 109731. Date of Electronic Publication: 2025 Jan 28.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE
Imprint Name(s):
Publication: New York : Elsevier
Original Publication: New York, Pergamon Press.
MeSH Terms:
Boosting Machine Learning Algorithms* , Glaucoma*/drug therapy , Quantitative Structure-Activity Relationship*, Humans ; Algorithms ; Machine Learning
Contributed Indexing:
Keywords: Degree-based indices; Extreme Gradient Boosting; Glaucoma; Regression model
Entry Date(s):
Date Created: 20250129 Date Completed: 20250504 Latest Revision: 20250529
Update Code:
20250529
DOI:
10.1016/j.compbiomed.2025.109731
PMID:
39879884
Database:
MEDLINE

Weitere Informationen

Glaucoma is an irreversible, progressive, degenerative eye disorder arising because of increased intraocular pressure, resulting in eventual vision loss if untreated. The QSPR relates, mathematically, by employing various algorithms, a specified property of a molecule that arises either from physical, chemical, or biological phenomena using various aspects of its structure. Here in, a similar application based on topological indices and inferences derived from the structure for the calculation of different drug properties like molar refractivity, refractive index, enthalpy, boiling points, molecular weight, and polarizability is presented. Linear regression is developed between the features of QSPR, coupled with topological indices, and performance assessment is conducted in conjunction with an Extreme Gradient Boosting model. From the obtained results, one can draw out that the XGBoost model will give better results than those from simple regression methods, specifically for polarizability. Predicted values validate the experiments. This work shows how the combination of machine learning techniques and topological indices can be used to get the best out of predictive modeling.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)

Declaration of competing interest The authors declare that they have no declaration mean no experiment on human.