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

Madhwani, J.; Sankranarayanan, M.

doi:10.1101/2025.08.09.669467

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bioRxiv · 10.1101/2025.08.09.669467

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

Madhwani, J.·Sankranarayanan, M.

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Abstract

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.

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Madhwani, J., Sankranarayanan, M.. 2025-08-12. Machine Learning-Based Bioactivity Prediction of Potential PPAR-γ Agonists for the Management of Diabetes. https://doi.org/10.1101/2025.08.09.669467

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