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A python based algorithmic approach to optimize sulfonamide drugs via mathematical modeling.

Wakeel Ahmed1,2, Kashif Ali2, Shahid Zaman1

  • 1Department of Mathematics, University of Sialkot, Sialkot, 51310, Pakistan.

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|May 28, 2024
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This study uses graph theory and a Python algorithm to analyze sulfonamide drug structures. It reveals key relationships between molecular properties and drug activity, aiding in drug design.

Keywords:
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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Graph Theory

Background:

  • Sulfonamide drugs are crucial in medicine.
  • Understanding their structure-activity relationships is vital for drug development.
  • Novel computational approaches can enhance this understanding.

Purpose of the Study:

  • To explore the structural properties of eleven sulfonamide drugs using graph theory.
  • To establish quantitative structure-property relationships (QSPR) for these drugs.
  • To identify significant correlations between topological indices and drug characteristics.

Main Methods:

  • Developing a Python algorithm to calculate topological indices for chemical graphs representing sulfonamide drugs.
  • Applying quantitative structure-property relationship (QSPR) methods.
  • Utilizing linear regression to model and predict structure-activity relationships.

Main Results:

  • Identification of significant relationships between topological indices and sulfonamide drug properties.
  • Development of a predictive model for drug characteristics based on molecular structure.
  • Insights into how specific structural features influence drug activity.

Conclusions:

  • The combination of topological indices, graph theory, and statistical models provides a robust framework for understanding sulfonamide drugs.
  • This approach advances pharmaceutical research and development by offering insights for drug design and optimization.
  • The study highlights the utility of computational methods in predicting drug behavior.