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Year
2025
Tech & Technique
Python, Random Forest, XGBoost, Flask API, Scikit-learn
Description
A drug-intelligence project focused on ADMET and physicochemical prediction using machine learning and in-silico workflows.
Key Highlights:
Key Highlights:
- Developed Random Forest and XGBoost pipelines for ADMET prediction tasks
- Applied molecular feature engineering using Morgan fingerprints
- Validated model quality with cross-validation and domain-aware checks
- Exposed predictions through a Flask API for real-time simulation-style usage
- Inspired by broader in-silico formulation and network-pharmacology workflows
My Role
ML and Backend Developer
- Built the model training and evaluation workflow
- Implemented feature engineering and validation strategy
- Exposed predictions through a production-ready Flask API