| --- |
| license: mit |
| tags: |
| - protein-generation |
| - antimicrobial-peptides |
| - flow-matching |
| - protein-design |
| - esm |
| - amp |
| library_name: pytorch |
| --- |
| |
| # FlowFinal: AMP Flow Matching Model |
|
|
| FlowFinal is a state-of-the-art flow matching model for generating antimicrobial peptides (AMPs). The model uses continuous normalizing flows to generate protein sequences in the ESM-2 embedding space. |
|
|
| ## Model Description |
|
|
| - **Model Type**: Flow Matching for Protein Generation |
| - **Domain**: Antimicrobial Peptide (AMP) Generation |
| - **Base Model**: ESM-2 (650M parameters) |
| - **Architecture**: Transformer-based flow matching with classifier-free guidance (CFG) |
| - **Training Data**: Curated AMP dataset with ~7K sequences |
|
|
| ## Key Features |
|
|
| - **Classifier-Free Guidance (CFG)**: Enables controlled generation with different conditioning strengths |
| - **ESM-2 Integration**: Leverages pre-trained protein language model embeddings |
| - **Compression Architecture**: Efficient 16x compression of ESM-2 embeddings (1280 → 80 dimensions) |
| - **Multiple CFG Scales**: Support for no conditioning (0.0), weak (3.0), strong (7.5), and very strong (15.0) guidance |
|
|
| ## Model Components |
|
|
| ### Core Architecture |
| - `final_flow_model.py`: Main flow matching model implementation |
| - `compressor_with_embeddings.py`: Embedding compression/decompression modules |
| - `final_sequence_decoder.py`: ESM-2 embedding to sequence decoder |
|
|
| ### Trained Weights |
| - `final_compressor_model.pth`: Trained compressor (315MB) |
| - `final_decompressor_model.pth`: Trained decompressor (158MB) |
| - `amp_flow_model_final_optimized.pth`: Main flow model checkpoint |
|
|
| ### Generated Samples (Today's Results) |
| - Generated AMP sequences with different CFG scales |
| - HMD-AMP validation results showing 8.8% AMP prediction rate |
|
|
| ## Performance Results |
|
|
| ### HMD-AMP Validation (80 sequences tested) |
| - **Total AMPs Predicted**: 7/80 (8.8%) |
| - **By CFG Configuration**: |
| - No CFG: 1/20 (5.0%) |
| - Weak CFG: 2/20 (10.0%) |
| - Strong CFG: 4/20 (20.0%) ← Best performance |
| - Very Strong CFG: 0/20 (0.0%) |
|
|
| ### Best Performing Sequences |
| 1. `ILVLVLARRIVGVIVAKVVLYAIVRSVVAAAKSISAVTVAKVTVFFQTTA` (No CFG) |
| 2. `EDLSKAKAELQRYLLLSEIVSAFTALTRFYVVLTKIFQIRVKLIAVGQIL` (Weak CFG) |
| 3. `IKLSRIAGIIVKRIRVASGDAQRLITASIGFTLSVVLAARFITIILGIVI` (Strong CFG) |
|
|
| ## Usage |
|
|
| ```python |
| from generate_amps import AMPGenerator |
| |
| # Initialize generator |
| generator = AMPGenerator( |
| model_path="amp_flow_model_final_optimized.pth", |
| device='cuda' |
| ) |
| |
| # Generate AMP samples |
| samples = generator.generate_amps( |
| num_samples=20, |
| num_steps=25, |
| cfg_scale=7.5 # Strong CFG recommended |
| ) |
| ``` |
|
|
| ## Training Details |
|
|
| - **Optimizer**: AdamW with cosine annealing |
| - **Learning Rate**: 4e-4 (final) |
| - **Epochs**: 2000 |
| - **Final Loss**: 1.318 |
| - **Training Time**: 2.3 hours on H100 |
| - **Dataset Size**: 6,983 samples |
|
|
| ## Files Structure |
|
|
| ``` |
| FlowFinal/ |
| ├── models/ |
| │ ├── final_compressor_model.pth |
| │ ├── final_decompressor_model.pth |
| │ └── amp_flow_model_final_optimized.pth |
| ├── generated_samples/ |
| │ ├── generated_sequences_20250829.fasta |
| │ └── hmd_amp_detailed_results.csv |
| ├── src/ |
| │ ├── final_flow_model.py |
| │ ├── compressor_with_embeddings.py |
| │ ├── final_sequence_decoder.py |
| │ └── generate_amps.py |
| └── README.md |
| ``` |
|
|
| ## Citation |
|
|
| If you use FlowFinal in your research, please cite: |
|
|
| ```bibtex |
| @misc{flowfinal2025, |
| title={FlowFinal: Flow Matching for Antimicrobial Peptide Generation}, |
| author={Edward Sun}, |
| year={2025}, |
| url={https://huggingface.co/esunAI/FlowFinal} |
| } |
| ``` |
|
|
| ## License |
|
|
| This model is released under the MIT License. |
|
|
