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README.md

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+# ProfileBFN
+Official implementation of ICLR 2025 ["ProfileBFN: Steering Protein Family Design through Profile Bayesian Flow"](https://openreview.net/forum?id=PSiijdQjNU&noteId=sRV2quHqPd).
+
+## Environment
+The environment is based on PyTorch 1.13. Follow the [official installation instructions](https://pytorch.org/get-started/previous-versions/) to set it up according to your CUDA version. Then, install the following packages:
+
+```bash
+pip install omegaconf hydra-core bitarray rdkit-pypi scipy lmdb numba scikit-learn
+```
+
+More detailed environment settings are located in env.yaml
+
+
+-----
+
+## Data
+Data used for evaluating the model is already put in the `data` folder
+
+---
+
+
+## Checkpoints
+We provide the pretrained checkpoint as [ProfileBFN_150M.ckpt](https://huggingface.co/hhhhhhh789/ProfileBFN_150M) and [ProfileBFN_650M.ckpt](https://huggingface.co/hhhhhhh789/ProfileBFN_650M), please download all files and set the CKPT_PATH to the corresponding directory. 
+
+
+## Sampling
+`mkdir ./results` All Generation Results will be placed in such subdir.
+
+Run `make sample_profile -f scripts.mk` to sample protein family based MSA. Note that inputs with inconsistent lengths would be automatically aligned.
+
+Run `make sample_sequence -f scripts.mk` to sample protein family based on single protein sequence.
+
+
+## Evaluation
+### Evaluating generated protein family by CCMPRED
+Clone [CCMPRED](https://github.com/jingjing-gong/contact_evaluation) repo in dir `test/ccmpred` and follow instructions as their README.  
+
+targets are generated sequence under `results/sample_profile` dir after the sampling process
+```bash
+cd test/ccmpred
+docker build -f docker/Dockerfile -t exp/contact_evaluation .
+CUDA_VISIBLE_DEVICES=4,5,6,7 ./scripts/run_evaluate.sh -i <input_dir> -o <output_dir>
+```
+
+
+## Citation
+```bash
+@article{gong2025steering,
+  title={Steering Protein Family Design through Profile Bayesian Flow},
+  author={Gong, Jingjing and Pei, Yu and Long, Siyu and Song, Yuxuan and Zhang, Zhe and Huang, Wenhao and Cao, Ziyao and Zhang, Shuyi and Zhou, Hao and Ma, Wei-Ying},
+  journal={arXiv preprint arXiv:2502.07671},
+  year={2025}
+}
+

special_tokens_map.json

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+{
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32": {
+      "content": "<mask>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "tokenizer_class": "EsmTokenizer",
+  "unk_token": "<unk>"
+}