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Quantitative structure-properties relationship analysis of Eigen-value-based indices using COVID-19 drugs structure
Abdul Rauf1, Muhammad Naeem2, Asia Hanif1
1Department of Mathematics Air University, Multan Campus Multan Pakistan.
International Journal of Quantum Chemistry
|January 31, 2023
Summary
This study explores quantitative structure-property relationship (QSPR) analysis for COVID-19 drugs using eigenvalue-based topological indices. Specific indices accurately predict key physical properties like molar reactivity and molecular weight.
Area of Science:
- Cheminformatics
- Medicinal Chemistry
- Computational Chemistry
Background:
- Topological indices are crucial for analyzing chemical structure topology.
- Quantitative structure-property relationship (QSPR) models molecular characteristics using numerical descriptors.
- Understanding drug properties is vital for developing effective therapeutics.
Purpose of the Study:
- To perform QSPR analysis on COVID-19 drugs.
- To evaluate the predictive power of eigenvalue-based topological indices.
- To identify key indices for predicting physical properties of COVID-19 drugs.
Main Methods:
- Utilized MATLAB for computing eigenvalue-based topological indices.
- Employed SPSS for statistical analysis of QSPR models.
- Correlated topological indices with physical properties (molar reactivity, polar surface area, molecular weight).
Main Results:
- Identified specific eigenvalue-based topological indices as significant predictors.
- Positive inertia index emerged as the best predictor for molar reactivity.
- Signless Laplacian Estrada index and Randić energy accurately predicted polar surface area and molecular weight, respectively.
Conclusions:
- Eigenvalue-based topological indices offer valuable insights into COVID-19 drug properties.
- QSPR analysis successfully links molecular topology to physical characteristics.
- This approach aids in predicting and understanding drug behavior.
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