Machine Learning Methodologies for Forecasting Drug Properties and Identifying Disease Indicators

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.