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<p>In this research, quantitative structure property relationship (QSPR) regression along with multi criteria decision making (MCDM) approaches, including TOPSIS and Simple Additive Weighting (SAW), is deployed to rank the antihematological cancer drugs by structural and physicochemical attributes. Topological indices like zagreb, Randic and Atom Bond Connectivity (ABC) indices are calculated with the Maple program and chemspider was employed to obtain physicochemical descriptors such as boiling point; molar refraction; polarizability and molar volume which are both modeled using cubic and logarithm regression. The cubic model performed better than the logarithmic models, both in terms of correlation coefficients and predictive accuracy. In the present study, two compounds with more complexity of structure and greater connectivity, Carfilzomib and Zanubrutinib were ranked higher by QSPR and MCDM model considering the structural features compared to those with simpler molecular frameworks including Cyclophosphamide and Cytarabine which was placed lower. The above results suggest that compositing QSPR regression and MCDM is applicable towards computationally automatic, yet efficient process for screening drug candidates systematically at the beginning stages of hematologic cancer therapies.</p>

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Multi criterion decision making analysis of hematologic cancer drugs via topological indices and physicochemical properties

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月5日

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  • Lina Huang,
  • Saba Hanif,
  • Muhammad Kamran Siddiqui,
  • Muhammad Faisal Hanif,
  • Muhammad Farhan Hanif,
  • Mohamed Abubakar Fiidow

摘要

In this research, quantitative structure property relationship (QSPR) regression along with multi criteria decision making (MCDM) approaches, including TOPSIS and Simple Additive Weighting (SAW), is deployed to rank the antihematological cancer drugs by structural and physicochemical attributes. Topological indices like zagreb, Randic and Atom Bond Connectivity (ABC) indices are calculated with the Maple program and chemspider was employed to obtain physicochemical descriptors such as boiling point; molar refraction; polarizability and molar volume which are both modeled using cubic and logarithm regression. The cubic model performed better than the logarithmic models, both in terms of correlation coefficients and predictive accuracy. In the present study, two compounds with more complexity of structure and greater connectivity, Carfilzomib and Zanubrutinib were ranked higher by QSPR and MCDM model considering the structural features compared to those with simpler molecular frameworks including Cyclophosphamide and Cytarabine which was placed lower. The above results suggest that compositing QSPR regression and MCDM is applicable towards computationally automatic, yet efficient process for screening drug candidates systematically at the beginning stages of hematologic cancer therapies.