Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Exploring the Symmetry of Curvilinear Regression Models for Enhancing the Analysis of Fibrates Drug Activity through Molecular Descriptors

These authors contributed equally to this work.
Version 1 : Received: 2 May 2023 / Approved: 3 May 2023 / Online: 3 May 2023 (04:48:22 CEST)

A peer-reviewed article of this Preprint also exists.

Wazzan, S.; Ozalan, N.U. Exploring the Symmetry of Curvilinear Regression Models for Enhancing the Analysis of Fibrates Drug Activity through Molecular Descriptors. Symmetry 2023, 15, 1160. Wazzan, S.; Ozalan, N.U. Exploring the Symmetry of Curvilinear Regression Models for Enhancing the Analysis of Fibrates Drug Activity through Molecular Descriptors. Symmetry 2023, 15, 1160.

Abstract

The paper describes the use of topological indices in conjunction with high cholesterol drugs, specifically Fibrates, to predict their physicochemical properties and biological activities. Fibrates are known to lower high triglycerides, increase HDL cholesterol, and reduce the small dense fraction of LDL cholesterol. The study uses a quantitative structural-property relationships (QSPR) approach, which involves analyzing the relationships between physicochemical properties and topological indices using curvilinear regression. The QSPR model predicts the physicochemical properties of the drugs based on degrees and distances determined from topological indices. The study also conducted (DFT) calculations at the B3LYP/6-31G(d,p) level on the four investigated derivatives to gain insights into their optimized geometries, DOS plots, HOMO and LUMO orbital energies, and distribution. The theoretical results presented in the study suggest that the use of topological indices in QSPR models could provide a powerful tool for predicting the physicochemical properties and biological activities of molecules, including drugs. These findings could lead to the development of new cholesterol-lowering drugs with desirable properties.

Keywords

Topological indices; Fibrates; Curvilinear regression; QSPR analysis

Subject

Computer Science and Mathematics, Mathematics

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