QuantaFold

Advanced protein family classification system achieving exceptional accuracy through cutting-edge ML techniques and GPU optimization.

Overview

QuantaFold is a state-of-the-art protein family classification system that achieves high accuracy on protein family classification through ESM-2 model fine-tuning with advanced optimization techniques.

Key Achievements

🏆 Competition Victory

Won MIT Hack Nation AI in intensive 17-hour development sprint
Competed against top-tier technical talent from leading universities
Demonstrated rapid prototyping and execution under pressure
Selected for SC25 poster presentation at International Conference for High Performance Computing

🎯 Technical Excellence

97.9% accuracy on 1,000 common protein families classification
Advanced GPU memory optimization using FP16 mixed-precision
Gradient accumulation and 8-bit AdamW optimizer implementation
Successfully trained on 400K-sequence dataset within compute constraints

🚀 Scalability Achievement

Scaled to 5,000-class imbalanced problem achieving 60% accuracy
Demonstrated understanding of real-world ML challenges beyond academic benchmarks
Implemented techniques for handling extreme class imbalance
Showcased practical machine learning engineering skills

Technical Implementation

Machine Learning Architecture

Foundation Model: ESM-2 (Evolutionary Scale Modeling) protein language model
Fine-tuning Approach: Low-Rank Adaptation (LoRA) for efficient parameter updates
Optimization Strategy: Mixed-precision training with automatic loss scaling
Memory Management: Gradient accumulation for effective large batch training

GPU Optimization Techniques

FP16 Mixed-Precision: Reduced memory usage while maintaining model accuracy
Gradient Accumulation: Enabled larger effective batch sizes on limited hardware
8-bit AdamW Optimizer: Memory-efficient optimization with maintained performance
Dynamic Memory Allocation: Optimized GPU memory usage for maximum dataset size

Dataset Engineering

Scale: 400K protein sequences across multiple families
Preprocessing Pipeline: Sequence tokenization and embedding generation
Class Balancing: Advanced techniques for handling imbalanced protein family distributions
Validation Strategy: Stratified sampling ensuring robust performance metrics

Scientific Impact

Research Contributions

Novel Optimization Pipeline: Advanced GPU memory management techniques for protein ML
Scalability Insights: Demonstrated approaches for handling massive protein classification tasks
Practical Applications: Real-world implications for computational biology and drug discovery
Academic Recognition: Selected for SC25 poster presentation

Computational Biology Applications

Protein Function Prediction: Enabling rapid classification of unknown protein sequences
Drug Discovery Support: Facilitating identification of therapeutic targets
Evolutionary Analysis: Supporting research into protein family relationships
Biotechnology Applications: Accelerating enzyme engineering and design

Development Methodology

Rapid Prototyping Strategy

Time-boxed Development: 17-hour sprint with iterative improvement cycles
Incremental Testing: Continuous validation throughout development process
Performance Monitoring: Real-time accuracy tracking and optimization
Resource Management: Efficient use of limited computational resources

Engineering Best Practices

Modular Architecture: Clean separation of data processing, model training, and evaluation
Version Control: Systematic tracking of experiments and model iterations
Documentation: Comprehensive code documentation for reproducibility
Error Handling: Robust exception handling for production-ready code

Performance Metrics

Accuracy Achievements

1,000 Classes: 97.9% classification accuracy
5,000 Classes: 60% accuracy on imbalanced dataset
Cross-Validation: Consistent performance across multiple validation folds
Generalization: Strong performance on held-out test sequences

Computational Efficiency

Training Time: Optimized for rapid iteration within hackathon constraints
Memory Usage: Efficient GPU memory utilization enabling large-scale training
Inference Speed: Fast classification for real-time applications
Resource Scaling: Techniques applicable to larger computational environments

Technical Challenges Overcome

Memory Optimization

Successfully trained large protein language models on consumer-grade GPUs
Implemented advanced memory management techniques
Achieved optimal balance between model capacity and computational constraints
Developed reusable optimization strategies for future projects

Class Imbalance

Addressed extreme imbalance in protein family distributions
Implemented sophisticated sampling and weighting strategies
Maintained performance across rare and common protein families
Demonstrated understanding of real-world ML challenges

Future Applications

Research Extensions

Multi-modal Integration: Incorporating structural and functional protein data
Transfer Learning: Applying techniques to related biological classification tasks
Active Learning: Intelligent sample selection for continued model improvement
Ensemble Methods: Combining multiple models for enhanced performance

Industry Applications

Pharmaceutical Research: Supporting drug target identification and validation
Biotechnology: Enabling enzyme engineering and protein design
Academic Research: Facilitating large-scale protein analysis studies
Diagnostic Tools: Supporting development of protein-based diagnostic methods

Recognition and Impact

The QuantaFold project demonstrates exceptional technical execution, innovative problem-solving, and significant potential for scientific impact. The combination of competition victory, academic recognition through SC25 selection, and practical advances in protein classification establishes this as a standout achievement in computational biology and machine learning engineering.

This project showcases the ability to deliver cutting-edge technical solutions under intense pressure while addressing real-world scientific challenges with immediate practical applications.