The project outlines using Bioinformatics, AI and Quantum Machine Learning to find Acetylcholinesterase (AChE) inhibitors as targets in Alzheimer’s disease (AD).

Drug discovery is the process of identifying chemical entities that are good therapeutic targets. My proposal outlines using Quantum Machine Learning to find Acetylcholinesterase (AChE) inhibitors as targets in Alzheimer’s disease (AD). Machine Learning and AI help find the most feasible targets in the least amount of time, leading to cost benefits for pharmaceuticals through the clinical trial phase. AD is the most common form of dementia causing cognitive disabilities for millions of people around the world. AChE inhibitors are a common treatment for AD, examples of drugs include donepezil, galantamine, and rivastigmine. These drugs compensate for the death of cholinergic neurons and offer symptomatic relief.

• ./Classical/ML model - contains Random Forest ML model
• ./Dataset/Part 3 - contains the preprocessed dataset