Graph Attention Network (GAT) Project

This repository contains my implementation of a multi-head Graph Attention Network (GAT) built for a Kaggle competition with a 17 million-row binding-affinity dataset. Developed in PyTorch Geometric, it predicts molecular interactions, achieving 92.81% accuracy and a 29% F1 score improvement (0.61 → 0.79). Optimized for an RTX 4080 with VRAM constraints.

Data Files

• Test and Train files can be found here: https://www.kaggle.com/competitions/leash-BELKA/data
• Files created by notebook (Such as gat model, graph strucutes, etc) not included as they are created by notebook.

Overview

• Duration: May 2024 – September 2024
• Goal: Predict binding affinity of molecules using graph-based ML on a massive dataset (~500K training graphs, 1.67M test molecules).
• Key Results:
  • Accuracy: 92.81%
  • F1 Score: Improved from 0.61 to 0.79 (+29%)
  • Inference enabled on 1.67M test molecules via data chunking

Features

• Multi-head GAT: Leverages attention mechanisms for molecular graph modeling.
• Class Imbalance Handling: Weighted loss and gradient clipping for stability.
• Memory Optimization: Chunked dataset into 4 partitions (~418K graphs each) to fit RTX 4080 VRAM, hitting 90% utilization without crashes.

Tech Stack

• Languages: Python
• Frameworks: PyTorch, PyTorch Geometric, NumPy, Pandas
• Hardware: NVIDIA RTX 4080
• Tools: Git, Command Line