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双三次回归分析：一种基于度熵估计皮肤癌药物物理化学性质的新方法。

Bicubic regression analysis a novel approach for estimation of physicochemical properties of skin cancer drugs through degree based entropy.

作者信息

Hakami Khalil Hadi, Khan Abdul Rauf, Zia Iqra

机构信息

Department of Mathematics, Faculty of Science, Jazan University, P.O. Box 2097, Jazan 45142, Kingdom of Saudi Arabia.

Department of Mathematics, Faculty of Sciences, Ghazi University, Dera Ghazi Khan, 32200, Pakistan.

出版信息

Heliyon. 2024 Oct 10;10(20):e39084. doi: 10.1016/j.heliyon.2024.e39084. eCollection 2024 Oct 30.

DOI:10.1016/j.heliyon.2024.e39084
PMID:39640615
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11620050/
Abstract

Skin cancer poses a significant risk to the healthcare system worldwide and is projected to increase substantially over the next two decades, particularly if not detected in its early stages. The primary aim of this study is to construct a quantitative structure-property relationship (QSPR) by correlating calculated entropies with topological indices and specific physical-chemical properties of pharmaceuticals, in order to enhance their usefulness. The bicubic regression model is constructed through degree-based topological entropies to perform the QSPR analysis for the prediction of physiochemical properties like polar surface area, complexity, molar refractivity, boiling point, and polarity of skin cancer drugs. It is also examined that degree-based entropies provide best-fit models for skin cancer physiochemical properties. We inspected five physio-chemical properties of these anticancer drugs and found this methodology suitable for predicting best-fit approximations regarding value. It is expected that this approach will be very favourable to examining problems related to mathematical chemistry.

摘要

皮肤癌对全球医疗保健系统构成重大风险，预计在未来二十年内将大幅增加，尤其是在早期未被发现的情况下。本研究的主要目的是通过将计算出的熵与药物的拓扑指数和特定物理化学性质相关联，构建定量结构-性质关系（QSPR），以提高其有用性。通过基于度的拓扑熵构建双三次回归模型，对皮肤癌药物的物理化学性质如极性表面积、复杂度、摩尔折射率、沸点和极性进行QSPR分析预测。还研究发现基于度的熵为皮肤癌物理化学性质提供了最佳拟合模型。我们检查了这些抗癌药物的五种物理化学性质，发现该方法适用于预测关于 值的最佳拟合近似值。预计这种方法将非常有利于研究与数学化学相关的问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/018d41a8f0e5/gr008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/f81e972ac179/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/d0c51dd7377f/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/d33fbd82cc86/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/37f2abdf88b2/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/2bfa0c517f69/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/c8be203035ac/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/4ec248ba7ed2/gr007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/018d41a8f0e5/gr008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/f81e972ac179/gr001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/d0c51dd7377f/gr002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/d33fbd82cc86/gr003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/37f2abdf88b2/gr004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/2bfa0c517f69/gr005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/c8be203035ac/gr006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/4ec248ba7ed2/gr007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8cf4/11620050/018d41a8f0e5/gr008.jpg

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