| # TR2-D2 For Multi-Objective Therapeutic Peptide Design 🧫 | |
| This part of the code is for finetuning a peptide MDM to optimize multiple therapeutic properties, including binding affinity to a protein target, solubility, non-hemolysis, non-fouling, and cell membrane permeability, with TR2-D2. | |
| The codebase is partially built upon [PepTune (Tang et.al, 2024)](https://arxiv.org/abs/2412.17780), [MDLM (Sahoo et.al, 2023)](https://github.com/kuleshov-group/mdlm), and [MDNS (Zhu et.al, 2025)](https://arxiv.org/abs/2508.10684). | |
| ## Environment Installation | |
| ``` | |
| conda env create -f environment.yml | |
| conda activate tr2d2-pep | |
| ``` | |
| ## Model Pretrained Weights Download | |
| Follow the steps below to download the model weights requried for this experiment, which is originally from [PepTune](https://arxiv.org/abs/2412.17780). | |
| 1. Download the PepTune pre-trained MDLM and place in `/TR2-D2/peptides/pretrained/`: https://drive.google.com/file/d/1oXGDpKLNF0KX0ZdOcl1NZj5Czk2lSFUn/view?usp=sharing | |
| 2. Download the pre-trained binding affinity Transformer model and place in `/TR2-D2/tr2d2-pep/scoring/functions/classifiers/`: https://drive.google.com/file/d/128shlEP_-rYAxPgZRCk_n0HBWVbOYSva/view?usp=sharing | |
| ## Finetune with TR2-D2 | |
| After downloading the pretrained checkpoints, follow the steps below to run fine-tuning: | |
| 1. Fill in the `base_path` in `scoring/scoring_functions.py` and `diffusion.py`. | |
| 2. Fill in `HOME_LOC` to the base path where `TR2-D2` is located and `ENV_PATH` to the directory where your environment is downloaded in `finetune.sh`. | |
| 3. Create a path `tr2d2-pep/results` where the fine-tuning curves and generation results will be saved and `tr2d2-pep/checkpoints` for checkpoint saving. Also, create `tr2d2-pep/logs` where the training logs will be saved. | |
| 3. To specify a target protein, set `--prot_seq <insert amino acid sequence>` and `--prot_name <insert protein name>`. Default protein is Transferrin receptor (TfR). | |
| Run fine-tuning using `nohup` with the following commands: | |
| ``` | |
| chmod +x finetune.sh | |
| nohup ./finetune.sh > finetune.log 2>&1 & | |
| ``` | |
| Evaluation will run automatically after the specified number of fine-tuning epochs `--num_epochs` is finished. To summarize metrics, fill in `path` and `prot_name` in `metrics.py` and run: | |
| ``` | |
| python metrics.py | |
| ``` |