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<p>This study investigates the application of topological indices (TIs) in conjunction with Multi-Criteria Decision Making (MCDM) methods to establish a rigorous and quantifiable framework for drug candidate selection. Using a set of twelve antihistamines, six degree-based topological indices derived from molecular graph theory are computed to encode structural and physicochemical characteristics. These indices, which exhibit strong correlations with key molecular properties, serve as decision criteria within an integrated MCDM framework employing TOPSIS, VIKOR, and SAW ranking techniques. Four weighting methods Point Allocation, Mean Weight, Standard Deviation, and Entropy are applied to assign relative importance to each index. The results demonstrate that the integration of topological descriptors into MCDM significantly enhances the objectivity and reliability of compound ranking, with Ebastine consistently identified as the top candidate. This methodology presents a promising approach for early-stage drug screening, offering a systematic, data-driven strategy for prioritization in drug development and personalized medicine.</p>

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Integrating topological indices and multi-criteria decision making for systematic ranking of antihistamines

  • 期刊论文

  • 发表日期：

    2026年3月26日

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  • Huang Wei,
  • Shamaila Yousaf,
  • Momina Rehman,
  • Zaryab Afzal,
  • Adnan Aslam

摘要

This study investigates the application of topological indices (TIs) in conjunction with Multi-Criteria Decision Making (MCDM) methods to establish a rigorous and quantifiable framework for drug candidate selection. Using a set of twelve antihistamines, six degree-based topological indices derived from molecular graph theory are computed to encode structural and physicochemical characteristics. These indices, which exhibit strong correlations with key molecular properties, serve as decision criteria within an integrated MCDM framework employing TOPSIS, VIKOR, and SAW ranking techniques. Four weighting methods Point Allocation, Mean Weight, Standard Deviation, and Entropy are applied to assign relative importance to each index. The results demonstrate that the integration of topological descriptors into MCDM significantly enhances the objectivity and reliability of compound ranking, with Ebastine consistently identified as the top candidate. This methodology presents a promising approach for early-stage drug screening, offering a systematic, data-driven strategy for prioritization in drug development and personalized medicine.