| # FlowFinal: Comprehensive Technical Documentation | |
| This directory contains detailed technical documentation for the FlowFinal antimicrobial peptide generation model. | |
| ## Documentation Structure | |
| ### Core Architecture Components | |
| 1. **[Encoder Process](encoder_process.tex)** - ESM-2 contextual embedding extraction and preprocessing | |
| - Sequence validation and preprocessing pipeline | |
| - ESM-2 embedding extraction methodology | |
| - Statistical normalization procedures | |
| - Comprehensive algorithms for reproducibility | |
| 2. **[Compressor/Decompressor](compressor_decompressor.tex)** - Transformer-based compression architecture | |
| - Hourglass pooling and unpooling operations | |
| - 16× compression methodology (1280D → 80D) | |
| - Joint training procedures and optimization | |
| - Performance metrics and validation results | |
| 3. **[Flow Matching Model](flow_model_training.tex)** - 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 | |
| 4. **[Decoder Process](decoder_process.tex)** - 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 | |
| 5. **[CFG Dataset & Generation Pipeline](cfg_dataset_generation_pipeline.tex)** - 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 | |
| 6. **[Results Analysis & Conclusions](results_analysis_conclusions.tex)** - 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* | |