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<p>Research on allergy treatments depends heavily on the ability to predict the chemical and biological characteristics of pharmaceutical compounds. The evaluation of drug efficacy, safety, and biological activity is supported by topological indices (TIs), which provide insightful information about molecular structures. This study investigates the use of degree-based TIs for a range of anti-allergic drugs, such as cetirizine, epinephrine, diphenhydramine, and loratadine. We determine TIs for these medications using edge partitioning, and we use linear, quadratic, and cubic regression to estimate properties like density, molar volume, molar refractivity, boiling point, vapor pressure, and flash point using quantitative structure-property relationship (QSPR) models. We use three multi-criteria decision-making (MCDM) techniques ENTROPY, VIKOR, and COPRAS to improve drug selection decision-making. By comparing possible drug candidates, these techniques enable a thorough ranking of options according to TIs and physicochemical characteristics. The findings show how useful TIs and MCDM techniques are in promoting drug development and discovery for the treatment of allergies, providing a strong framework for choosing the most promising substances with the best therapeutic profiles.</p>

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Advanced Modeling and Decision Support for Anti-Allergic Drugs Using Topological Indices and QSPR Analysis

  • 期刊论文

  • 发表日期：

    2026年5月1日

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  • Muhammad Waheed Rasheed,
  • Abid Mahboob,
  • Asima Parveen,
  • Mona Bin-Asfour

摘要

Research on allergy treatments depends heavily on the ability to predict the chemical and biological characteristics of pharmaceutical compounds. The evaluation of drug efficacy, safety, and biological activity is supported by topological indices (TIs), which provide insightful information about molecular structures. This study investigates the use of degree-based TIs for a range of anti-allergic drugs, such as cetirizine, epinephrine, diphenhydramine, and loratadine. We determine TIs for these medications using edge partitioning, and we use linear, quadratic, and cubic regression to estimate properties like density, molar volume, molar refractivity, boiling point, vapor pressure, and flash point using quantitative structure-property relationship (QSPR) models. We use three multi-criteria decision-making (MCDM) techniques ENTROPY, VIKOR, and COPRAS to improve drug selection decision-making. By comparing possible drug candidates, these techniques enable a thorough ranking of options according to TIs and physicochemical characteristics. The findings show how useful TIs and MCDM techniques are in promoting drug development and discovery for the treatment of allergies, providing a strong framework for choosing the most promising substances with the best therapeutic profiles.