Predicting binding affinity of drug molecules with Machine Learning: Acetylcholinesterase Enzyme Study

Abstract

This study examines the machine learning studies on the acetylcholinesterase (AChE) enzyme and summarizes the results. The AChE enzyme is an enzyme that plays an important role in the nervous system and is responsible for the breakdown of a neurotransmitter called acetylcholine. Identifying potential drug candidates targeting the AChE enzyme is of great importance in the drug discovery process. In this study, the detection and evaluation of molecules interacting with the AChE enzyme were performed using machine learning techniques such as Support Vector Regressor, Random Forest Regressor, and Extreme Gradient Boosting. The results show that machine learning is an important tool for identifying and designing compounds that interact with the AChE enzyme. This work makes a valuable contribution to research focusing on the AChE enzyme in drug discovery and has the potential to develop more effective drugs in the future.