This project presents a Graph Neural Network (GNN)-based framework for predicting Drug-Drug Interactions (DDIs) using pretrained SMILES embeddings and Graph Attention Networks (GAT). Given the limitations of clinical studies and traditional computational methods in detecting complex DDIs.

Physics-informed machine learning and cheminformatics workflow for predicting biochar adsorption capacity of organic water contaminants using RDKit descriptors, molecular fingerprints, ChemBERTa embeddings, and a Streamlit research app.

This is a public repository for the Kaggle competition, Thermophysical Property: Melting Point. This project leverages ChemBERT to obtain SMILES embeddings for downstream regression models.