Abstract:
This 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 a drug, give a measurable assessment of the molecule’s
structure and are widely utilized in drug design and discovery. This study uses advanced
machine learning methods to improve the accuracy and efficiency of predicting the drug
property namely molecular weight, allowing for faster screening and optimization of med-
ication candidates. The number of patients registered to the hospitals diagnosed with liver
disorder is very high. ML approaches might be utilized to overcome the burden on the
doctors by developing accurate classifiers for disease prediction. It is also advantageous
to detect the key factors involved in the development of the disease so that precautionary
measures might be taken for prevention. This thesis is also focused to develop a machine
learning classifier to predict liver disease.
