The Challenge
Identifying PPARγ Modulators for Drug Discovery & Chemical Safety
PPARγ is a nuclear receptor central to glucose and lipid metabolism, making it a prime target for antidiabetic therapies. Identifying potent antagonists computationally is essential for both drug discovery and assessing environmental chemical risks, particularly for per- and polyfluoroalkyl substances (PFAS) that may disrupt endocrine function.
Existing computational approaches often treat binding affinity and biological activity separately, and many lack the regulatory validation needed for real-world decision-making in chemical safety assessment.
Our Approach
A synergistic dual-model strategy combining deep learning and consensus classification
Curate bioassay data and perform molecular docking
Gathered 6,587 compounds from PubChem Tox21 bioassay data and performed molecular docking against the PPARγ ligand-binding domain using the Enalos Asclepios KNIME pipeline to generate binding affinity scores.
Build deep learning binding affinity model
Trained a fully connected neural network on Extended Connectivity Fingerprints (ECFPs) to classify compounds as strong or weak binders based on molecular docking scores, providing a first-pass screen for PPARγ interaction.
Develop consensus classification for antagonism
Built three base classifiers — Random Forest, Support Vector Machine, and k-Nearest Neighbours — to predict antagonistic activity, then integrated them into an ensemble bagging strategy for improved accuracy.
Validate models to OECD guidelines
Rigorously validated both models according to OECD principles, ensuring generalisability and sufficient efficiency in detecting the minority class of active antagonists through cross-validation and external test sets.
Screen PFAS and deploy on Enalos Cloud
Applied the validated workflows to screen 34 commercially available PFAS compounds, forecasting their binding strength and antagonistic activity against PPARγ. Models deployed as interactive web applications on the Enalos Cloud Platform.