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Analysis of the physicochemical properties of cancer treatment drugs using an innovative approach: modified reverse degree-based topological descriptors

  • Published: 26 February 2026
  • MSC : 05C10, 05C31, 05C90, 05C92, 05C99

  • Chemical graph theory, in its theoretical forms, is important for designing and developing medicines because it looks at the structure of molecules. Topological descriptors are used to mathematically represent a chemical structure's topological properties, thereby enhancing the development of quantitative structure-activity relationship (QSPR) models for drugs and evaluating the efficacy of medications. The modified reverse degree is an innovative approach because it depends on the maximum degree of the graph and encompasses both the reverse and reduced reverse degrees of a graph as well. These are also effective for the examination of intricate molecular structures, but they also limit the analysis of analogous structures. The attributes enhance the sensitivity of the modified reverse degree-based topological indices, precisely predicting certain physicochemical aspects of various molecular structures. In this paper, we examine 17 drugs used to treat cancer. These include amathaspiramide E, aminopterin, aspidostomide E, carmustine, caulibugulone E, convolutamine F, melatonin, perfragilin A, podophyllotoxin, pterocellin B, raloxifene, tambjamine K, convolutamide A, convolutamydine A, daunorubicin, deguelin, and minocycline. QSPR analysis is performed using an innovative approach, using modified reverse degree-based topological descriptors, and estimates five physicochemical properties of these medicines. This study also provides an excellent correlation between the physicochemical properties and topological descriptors.

    Citation: Fairouz Tchier, Abdul Rauf Khan, Danish Ishaq, Yilun Shang. Analysis of the physicochemical properties of cancer treatment drugs using an innovative approach: modified reverse degree-based topological descriptors[J]. AIMS Mathematics, 2026, 11(2): 4705-4738. doi: 10.3934/math.2026192

    Related Papers:

  • Chemical graph theory, in its theoretical forms, is important for designing and developing medicines because it looks at the structure of molecules. Topological descriptors are used to mathematically represent a chemical structure's topological properties, thereby enhancing the development of quantitative structure-activity relationship (QSPR) models for drugs and evaluating the efficacy of medications. The modified reverse degree is an innovative approach because it depends on the maximum degree of the graph and encompasses both the reverse and reduced reverse degrees of a graph as well. These are also effective for the examination of intricate molecular structures, but they also limit the analysis of analogous structures. The attributes enhance the sensitivity of the modified reverse degree-based topological indices, precisely predicting certain physicochemical aspects of various molecular structures. In this paper, we examine 17 drugs used to treat cancer. These include amathaspiramide E, aminopterin, aspidostomide E, carmustine, caulibugulone E, convolutamine F, melatonin, perfragilin A, podophyllotoxin, pterocellin B, raloxifene, tambjamine K, convolutamide A, convolutamydine A, daunorubicin, deguelin, and minocycline. QSPR analysis is performed using an innovative approach, using modified reverse degree-based topological descriptors, and estimates five physicochemical properties of these medicines. This study also provides an excellent correlation between the physicochemical properties and topological descriptors.



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    [1] B. Figuerola, C. Avila, The phylum bryozoa as a promising source of anticancer drugs, Mar. Drugs, 17 (2019), 477. https://doi.org/10.3390/md17080477 doi: 10.3390/md17080477
    [2] W. Gao, W. F. Wang, M. R. Farahani, Topological indices study of molecular structure in anticancer drugs, J. Chem., 2016 (2016), 3216327. https://doi.org/10.1155/2016/3216327 doi: 10.1155/2016/3216327
    [3] D. E. Thurston, I. Pysz, Chemistry and pharmacology of anticancer drugs, 2 Eds., Boca Raton: CRC Press, 2021. https://doi.org/10.1201/9781315374727
    [4] E. Espinosa, P. Zamora, J. Feliu, M. G. Barón, Classification of anticancer drugs–a new system based on therapeutic targets, Cancer Treatment Rev., 29 (2003), 515–523. https://doi.org/10.1016/S0305-7372(03)00116-6 doi: 10.1016/S0305-7372(03)00116-6
    [5] S. Kumar, M. K. Ahmad, M. Waseem, A. K. Pandey, Drug targets for cancer treatment: an overview, Med. Chem., 5 (2015), 115–123.
    [6] S. Hayat, S. Wazzan, A computational approach to predictive modeling using connection-based topological descriptors: applications in coumarin anticancer drug properties, Int. J. Mol. Sci., 26 (2025), 1827. https://doi.org/10.3390/ijms26051827 doi: 10.3390/ijms26051827
    [7] M. C. Shanmukha, N. S. Basavarajappa, K. C. Shilpa, A. Usha, Degree-based topological indices on anticancer drugs with QSPR analysis, Heliyon, 6 (2020), e04235. https://doi.org/10.1016/j.heliyon.2020.e04235 doi: 10.1016/j.heliyon.2020.e04235
    [8] M. N. Husin, A. R. Khan, N. U. H. Awan, F. J. H. Campena, F. Tchier, S. Hussain, Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors, PLOS ONE, 19 (2024), e0302276. https://doi.org/10.1371/journal.pone.0302276 doi: 10.1371/journal.pone.0302276
    [9] O. C. Havare, Topological indices and QSPR modeling of some novel drugs used in the cancer treatment, Int. J. Quantum Chem., 121 (2021), e26813. https://doi.org/10.1002/qua.26813 doi: 10.1002/qua.26813
    [10] M. C. Shanmukha, A. Usha, B. M. Praveen, A. Douhadji, Degree‐based molecular descriptors and QSPR analysis of breast cancer drugs, J. Math., 2022 (2022), 5880011. https://doi.org/10.1155/2022/5880011 doi: 10.1155/2022/5880011
    [11] S. A. U. H. Bokhary, Adnan, M. K. Siddiqui, M. Cancan, On topological indices and QSPR analysis of drugs used for the treatment of breast cancer, Polycycl. Aromat. Comp., 42 (2022), 6233–6253. https://doi.org/10.1080/10406638.2021.1977353 doi: 10.1080/10406638.2021.1977353
    [12] X. L. Shi, S. Kosari, M. Ghods, N. Kheirkhahan, Innovative approaches in QSPR modeling using topological indices for the development of cancer treatments, PLOS ONE, 20 (2025), e0317507. https://doi.org/10.1371/journal.pone.0317507 doi: 10.1371/journal.pone.0317507
    [13] A. R. Khan, N. U. H. Awan, M. U. Ghani, S. M. Eldin, H. Karamti, A. H. Jawhari, et al., Fundamental aspects of skin cancer drugs via degree-based chemical bonding topological descriptors, Molecules, 28 (2023), 3684. https://doi.org/10.3390/molecules28093684 doi: 10.3390/molecules28093684
    [14] L. Huang, Y. Wang, K. Pattabiraman, P. Danesh, M. K. Siddiqui, M. Cancan, Topological indices and QSPR modeling of new antiviral drugs for cancer treatment, Polycycl. Aromat. Comp., 43 (2023), 8147–8170. https://doi.org/10.1080/10406638.2022.2145320 doi: 10.1080/10406638.2022.2145320
    [15] S. Yousaf, K. Shahzadi, Utilizing topological indices in QSPR modeling to identify non-cancer medications with potential anticancer properties: a promising strategy for drug repurposing, Front. Chem., 12 (2024), 1410882. https://doi.org/10.3389/fchem.2024.1410882 doi: 10.3389/fchem.2024.1410882
    [16] M. Arockiaraj, J. J. J. Godlin, S. Radha, T. Aziz, M. Al-Harbi, Comparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysis, Sci. Rep., 15 (2025), 3639. https://doi.org/10.1038/s41598-025-88044-x doi: 10.1038/s41598-025-88044-x
    [17] S. Zaman, Statistical evaluation of cancer drugs by QSPR modeling, Nano, 20 (2025), 2450115. https://doi.org/10.1142/S1793292024501157 doi: 10.1142/S1793292024501157
    [18] S. Zaman, H. S. A. Yaqoob, A. Ullah, M. Sheikh, QSPR analysis of some novel drugs used in blood cancer treatment via degree based topological indices and regression models, Polycycl. Aromat. Comp., 44 (2024), 2458–2474. https://doi.org/10.1080/10406638.2023.2217990 doi: 10.1080/10406638.2023.2217990
    [19] P. C. S. Costa, J. S. Evangelista, I. Leal, P. C. M. L. Miranda, Chemical graph theory for property modeling in QSAR and QSPR–charming QSAR & QSPR, Mathematics, 9 (2021), 60. https://doi.org/10.3390/math9010060 doi: 10.3390/math9010060
    [20] M. Ghorbani, Z. Vaziri, R. Alidehi-Ravandi, Y. L. Shang, The symmetric division Szeged index: a novel tool for predicting physical and chemical properties of complex networks, Heliyon, 11 (2025), e42280. https://doi.org/10.1016/j.heliyon.2025.e42280 doi: 10.1016/j.heliyon.2025.e42280
    [21] A. R. Khan, N. U. H. Awan, F. Tchier, S. D. Alahmari, A. F. Khalel, S. Hussain, An estimation of physiochemical properties of bladder cancer drugs via degree-based chemical bonding topological descriptors, J. Biomol. Struct. Dyn., 43 (2025), 1665–1673. https://doi.org/10.1080/07391102.2023.2292792 doi: 10.1080/07391102.2023.2292792
    [22] M. Arockiaraj, A. B. Greeni, A. R. A. Kalaam, Linear versus cubic regression models for analyzing generalized reverse degree based topological indices of certain latest corona treatment drug molecules, Int. J. Quantum Chem., 123 (2023), e27136. https://doi.org/10.1002/qua.27136 doi: 10.1002/qua.27136
    [23] V. R. Kulli, Reverse Zagreb and reverse hyper-Zagreb indices and their polynomials of rhombus silicate networks, Ann. Pure Appl. Math., 16 (2018), 47–51. http://dx.doi.org/10.22457/apam.v16n1a6 doi: 10.22457/apam.v16n1a6
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