• Saurabh Patil
• Vishal Ghule
• Pradip Shelake
• Rahul Akhade
• Suchitra Deokate

This study explores the application of machine learning (ML) techniques in drug discovery, focusing on predicting the potency (pIC50 values) of molecules targeting acetylcholinesterase (AChE), a critical enzyme implicated in various neurological disorders. Leveraging the ChEMBL database, a rich repository of bioactivity data, we employed a random forest regression model to predict the pIC50 values of molecules based on their chemical features. Our methodology involved data preprocessing, feature engineering, model training, and evaluation, culminating in the development of predictive models capable of prioritizing molecules with potential therapeutic efficacy against AChE-related diseases.

Drug Discovery, Machine Learning, Data Analysis, Random Forest.

"Drug Discovery Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.i138-i142, April-2024, Available :http://www.jetir.org/papers/JETIR2404818.pdf

"Drug Discovery Using ML", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppi138-i142, April-2024, Available at : http://www.jetir.org/papers/JETIR2404818.pdf