Utilizing Machine Learning for Predicting Potential JAK۱ Inhibitors Among FDA-Approved Drugs

This study used machine learning, specifically a Random Forest classifier, to identify potential JAK۱ inhibitors from a pool of FDA-approved drugs. By leveraging computational methods and extensive feature selection, the top ۱۰ most promising compounds were selected for further investigation, showcasing the power of data-driven approaches in accelerating drug discovery and repurposing processes.

کلیدواژه ها:

Machin learning ، Computational drug discovery ، Drug repurposing