ESM-IF1

from mindsponge import PipeLine
from mindsponge.common.config_load import load_config

pipe = PipeLine(name="ESM_IF1")
pipe.set_device_id(0)

# 第一次使用时未获取config文件，执行如下指令模型可自动下载config文件，后续使用可手动修改所需内容
# from mindsponge.pipeline.pipeline import download_config
# conf = download_config(pipe.config["sampling"], pipe.config_path + "sampling.yaml")
# pipe.initialize(conf=conf)

pipe.initialize(key="sampling")
pipe.model.from_pretrained()
res = pipe.predict(data={YOUR_PDB_PATH})
print(res)

from mindsponge import PipeLine

pipe = PipeLine(name="ESM_IF1")
pipe.set_device_id(0)
pipe.initialize("training")
pipe.train({YOUR_DATA_PATH}+"train_chain_set.jsonl", num_epochs=1)

@InProceedings{pmlr-v162-hsu22a,
  title = 	 {Learning inverse folding from millions of predicted structures},
  author =       {Hsu, Chloe and Verkuil, Robert and Liu, Jason and Lin, Zeming and Hie, Brian and Sercu, Tom and Lerer, Adam and Rives, Alexander},
  booktitle = 	 {Proceedings of the 39th International Conference on Machine Learning},
  pages = 	 {8946--8970},
  year = 	 {2022},
  editor = 	 {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
  volume = 	 {162},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {17--23 Jul},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v162/hsu22a/hsu22a.pdf},
  url = 	 {https://proceedings.mlr.press/v162/hsu22a.html},
}