FlowFinal: Comprehensive Technical Documentation
This directory contains detailed technical documentation for the FlowFinal antimicrobial peptide generation model.
Documentation Structure
Core Architecture Components
Encoder Process - ESM-2 contextual embedding extraction and preprocessing
- Sequence validation and preprocessing pipeline
- ESM-2 embedding extraction methodology
- Statistical normalization procedures
- Comprehensive algorithms for reproducibility
Compressor/Decompressor - Transformer-based compression architecture
- Hourglass pooling and unpooling operations
- 16× compression methodology (1280D → 80D)
- Joint training procedures and optimization
- Performance metrics and validation results
Flow Matching Model - Core generative model with CFG
- 12-layer transformer architecture with skip connections
- Classifier-Free Guidance implementation and theory
- H100-optimized training methodology
- CFG scale analysis and optimal conditioning
Decoder Process - ESM-2 language model head decoder
- Probabilistic sequence sampling (non-cosine approach)
- Nucleus sampling with temperature control
- Advantages over cosine similarity methods
- Implementation details and performance metrics
Pipeline Components
CFG Dataset & Generation Pipeline - Complete system pipeline
- Multi-source data integration and validation
- Strategic masking for CFG training
- Advanced ODE integration methods (DOPRI5, RK4, Euler)
- End-to-end generation with quality control
Results Analysis & Conclusions - Comprehensive experimental analysis
- Complete catalog of all 80 generated sequences
- Dual validation results (HMD-AMP + APEX)
- Physicochemical property analysis
- Performance insights and future directions
Key Results Summary
- Total Sequences Generated: 80 across 4 CFG scales
- HMD-AMP Success Rate: 8.8% overall, 20% for Strong CFG (scale 7.5)
- Optimal CFG Scale: 7.5 (balanced control and diversity)
- Training Efficiency: 2.3 hours convergence on H100 GPU
- Model Size: 607MB final checkpoint, 78M+ parameters
Mathematical Framework
All documentation includes:
- Complete mathematical formulations
- Detailed algorithmic descriptions
- Performance benchmarks and validation
- Implementation-ready pseudocode
- Comprehensive references and citations
Usage
These LaTeX files are designed for:
- Academic paper submission and peer review
- Technical documentation and reproducibility
- Educational materials for flow matching in proteins
- Implementation guidance for researchers
Model Availability
The complete FlowFinal model, weights, and datasets are available at: https://huggingface.co/esunAI/FlowFinal
Documentation generated on 2025-08-29 17:01:37 Total documentation: 6 comprehensive LaTeX files