This is a quantitative structure-activity relationship model QSAR for drug discovery which uses machine learning and data science for computational drug discovery. 1- A machine learning model using the ChEMBL bioactivity data. 2- performing Descriptor Calculation and Exploratory Data Analysis. 3- calculating molecular descriptors that are essentially quantitative descriptions of the compounds in the dataset. Finally, prepare this into a dataset for subsequent model building. 4- building a regression model of acetylcholinesterase inhibitors using the random forest algorithm. 5- comparing several ML algorithms for building regression models of acetylcholinesterase inhibitors.
First, the data collection, descriptor calculation and EDA was performed based on SARS Covid Virus and later for simplicity and due to the availability of complete results Acetylcholines were selected for further calculating and comparing the regression models.