Selected Topological Indices with Applications to the Identification of Anti-breast Cancer Agents

Simon Mukwembi, Farai Nyabadza and Opeyemi Dorcas Ogundeji
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Simon Mukwembi: University of Witwatersrand, South Africa
Farai Nyabadza: University of Johannesburg, South Africa
Opeyemi Dorcas Ogundeji: African Institute for Mathematical Sciences (AIMS), Cape Town, South Africa

International Journal of Research and Scientific Innovation, 2023, vol. 10, issue 1, 96-109

Abstract: Breast Cancer is a global challenge that mostly affects women and its treatment is stage dependent. The treatment, among other methods, involves the use of medicines that shrink or kill the cancer cells. This type of treatment involves the use of chemical agents. In this research, the graph theoretic techniques to screen and identify chemical compounds with anti-breast cancer activities were used. In particular, the distance and graph based topological indices were used. In this research, we developed a graph theory model that screens compounds for anti-breast cancer activity, and we also provide an assessment of the quality of model that was developed. Our newly developed graph theoretic model can predicts IC50 values of compounds for anti-breast cancer activity. We also recommended that normalized diameter is one of the key parameter in developing a model to predict some useful flavonoids that have anti-breast cancer activity. We presented the upper bound on the known indices and explored their applications in the prediction of cytotoxic activity of flavonoids against breast cancer cells in drug design. A model that fits to data generated in the laboratories was formulated and analyzed. We did simulation on the correlations between parameters and IC50 values. Conclusions on relationship of activities and graphical structures will be made. The implications of relationship on the identification of compounds with the most activity against breast cancer are also discussed.

Date: 2023
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