Abstract:
his thesis investigates the use of machine learning approaches to predict and analyze
drug properties in terms of topological indices, which are important for understanding their
chemical and biological characteristics. Topological indices, generated from graphical rep-
resentation of chemical formation of drugs, give a measurable assessment of the molecule’s
structure and are widely utilized in drug design and discovery. Firstly, we compute the topo-
logical indices of various cancerous drugs including Randic, Zagreb, Nirmala, geometric
quadratic and quadratic geometric indices. Afterwards, we find the physical measures like
molecular weight of these drugs. Then, we conduct the correlation analysis to capture the
relationship between each pair of attributes in the data. Especially, to see the linear re-
lationship between molecular weight and indices. Firstly, we develop different machine
learning models to check which fits the best. The performance of the model is tested using
mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE)
and the sum of squared errors (R2). Mainly, we have focused on (R2). Further, we show
graphical representation of few of these models. This study uses advanced machine learn-
ing methods to improve the accuracy and efficiency of predicting the drug property namely
molecular weight, allowing for faster screening and optimization of medication candidates.
These findings might be helpful for investigating cancerous drugs used in the thesis based
on topological indices at a deeper level.
