Machine Learning-Based Bioactivity Prediction of Potential PPAR-γ Agonists for the Management of Diabetes

This research paper presents a machine learning approach to predict bioactivity of compounds that can act as PPAR-gamma agonist, a critical target for diabetes treatment. Using data from the ChEMBL database, molecular descriptors were calculated and a Random Forest model was developed, achieving an R2 score of 0.83. Key molecular features influencing bioactivity were identified, and a web application was created for real-time predictions. This approach demonstrates how computational methods can accelerate drug discovery for diabetes treatment.