Predicting anti-cancer activity in flavonoids: a graph theoretic approach

Simon Mukwembi, Farai Nyabadza

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In drug design, there are two major causes of drug failure in the clinic. First, the drug has to work, and second, the drug should be safe. Identifying compounds that work for certain ailments require enormous experimental time and, in general, is cost intensive. In this paper, we are concerned with melanoma, a special type of cancer that affects the skin. In particular, we seek to provide a mathematical model that can predict the ability of flavonoids, a vast and natural class of compounds that are found in plants, in reversing or alleviating melanoma. The basis for our model is the conception of a new graph parameter called, for lack of better terminology, graph activity, which captures melanoma cancer healing properties of the flavonoids. With a superior coefficient of determination, R2= 1 , the new model faithfully reproduces anti-cancer activities of some known data-sets. We demonstrate that the model can be used to rank the healing abilities of flavonoids which could be a powerful tool in the screening, and identification, of compounds for drug candidates.

Original languageEnglish
Article number3309
JournalScientific Reports
Issue number1
Publication statusPublished - Dec 2023

ASJC Scopus subject areas

  • Multidisciplinary


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