Computational Notebooks & Tools (In Progress)
I am currently building and documenting computational biology workflows, machine learning models for bioactivity prediction, and paper reproductions using Python, RDKit, and PyTorch.
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Overview
This section will host open Jupyter notebooks, reproducible code repositories, and interactive cheminformatics tools as they are completed.
The Problem
Translating biological data into machine learning models requires rigorous data cleaning, descriptor calculation, and model validation.
Methodology
Workflows are built in Python using specialized libraries like Biopython, RDKit, and scikit-learn, with code structured for open reproduction.
Results
First project notebooks and interactive dashboards will be published here soon.
Future Directions
Initial releases will focus on bioactivity prediction pipelines and molecular docking benchmarks.