Prediction of Molecular and Physical Properties of Non-small Cell Lung Cancer (NSCLC) Drugs using Mathematical Modelling and M-Polynomial Indices

Faiqa Suleman; Aamir Shahzad; Tasadduq Niaz; Muhammad Ali; Sidra Ashraf

Authors

Faiqa Suleman Faculty of Sciences, Superior University Lahore, Lahore 54000, Pakistan
Aamir Shahzad 2Department of Mathematics, Faculty of Natural Science and Technology, Baba Guru Nanak University, Nankana Sahib 39100, Pakistan
Tasadduq Niaz Faculty of Sciences, Superior University Lahore, Lahore 54000, Pakistan
Muhammad Ali Department of Pharmacology, College of Pharmacy, University of Sargodha, Sargodha 40100, Pakistan
Sidra Ashraf Department of Mathematics, Faculty of Sciences, University of Sargodha, Sargodha 40100, Pakistan

Keywords:

M Polynomial Indices, Statistical Analysis, NSCLC, QSPR, Physical Properties

Abstract

The computation of M-Polynomial indices for Erlotinib, a tyrosine kinase receptor inhibitor and most widely recognized anti-cancer drug for the treatment of patients with NSCLC and advance pancreatic cancer is the main focus of this study. In order to efficiently calculate these M-polynomial indices, we used a graph-based method which renders use of the edge partitioning technique based on adjacent matrices and vertex degrees. Using Python software, we applied numerous regression models, such as numerous Linear Regression (LR), Elastic Net Regression (ENR), Lasso Regression (LR), Ridge Regression (RR) and Support Vector Regression (SVR), to develop Quantitative Structure-Property Relationships (QSPR). Based on the M polynomial indices, these models were utilized to forecast the physical properties such as melting point, enthalpy of vaporization, molar refractivity, molar volume, and polarizability, molecular weight, molecular mass, surface area, chemical hardness of NSCLC medications. According to our research, the M-polynomial indices predict these physical attributes with remarkable accuracy, providing crucial information on structural traits that maximize anticancer effectiveness. Additionally, we suggested predictive models for every physical attribute examined, proving the value of the M-polynomial index in comprehending molecular behaviour and directing the creation of innovative therapeutic medicines. This study not only facilitates the accurate prediction of physical properties for known NSCLC drugs but also holds the potential to fasten the novel drug discovery and development, uncharacterized anti-cancer compounds, thus contributing to the advancement of cancer therapeutics.

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