BALM

生物啟發的抗體語言模型（BALM）

該存儲庫包含推理代碼，用於使用生物啟發的抗體語言模型準確預測抗體功能和結構

創建環境

 conda env create -f environment.yml
conda activate BALM

準備

可以從Google Drive鏈接下載脂肪的預訓練。

運行推理

 from modeling_balm import BALMForMaskedLM
from ba_position_embedding import get_anarci_pos
from transformers import EsmTokenizer
import torch

# an antibody sequence example
input_seq = "AVQLQESGGGLVQAGGSLRLSCTVSARTSSSHDMGWFRQAPGKEREFVAAISWSGGTTNYVDSVKGRFDISKDNAKNAVYLQMNSLKPEDTAVYYCAAKWRPLRYSDNPSNSDYNYWGQGTQVTVSS"

tokenizer = EsmTokenizer.from_pretrained("./tokenizer/vocab.txt", do_lower_case=False, model_max_length=168)

tokenizer_input = tokenizer(input_seq, truncation=True, padding="max_length", return_tensors="pt")
# generate position_ids
tokenizer_input.update(get_anarci_pos(input_seq))

with torch.no_grad():
    # please download from Google drive link before
    model = BALMForMaskedLM.from_pretrained("./pretrained-BALM/")
    # on CPU device
    outputs = model(**tokenizer_input, return_dict=True, output_hidden_states=True, output_attentions=True)

    # final hidden layer representation [batch_sz * max_length * hidden_size]
    final_hidden_layer = outputs.hidden_states[-1]
    
    # final hidden layer sequence representation [batch_sz * hidden_size]
    final_seq_embedding = final_hidden_layer[:, 0, :]
    
    # final layer attention map [batch_sz * num_head * max_length * max_length]
    final_attention_map = outputs.attentions[-1]

Balmfold服務器

Balmfold基於BALM，以預測具有一級序列的抗體三級結構。在Balmfold Server可以免費獲得在線服務器。嘗試一下。 :)

引用

如果您發現我們的模型對您有用，請引用為：

 @article{jing2024accurate,
  title={Accurate prediction of antibody function and structure using bio-inspired antibody language model},
  author={Jing, Hongtai and Gao, Zhengtao and Xu, Sheng and Shen, Tao and Peng, Zhangzhi and He, Shwai and You, Tao and Ye, Shuang and Lin, Wei and Sun, Siqi},
  journal={Briefings in Bioinformatics},
  volume={25},
  number={4},
  pages={bbae245},
  year={2024},
  publisher={Oxford University Press}
}

香脂的體系結構和預訓練過程建立在ESM上，並擁抱面部建模框架。我們非常感謝ESM的工作和擁抱面孔團隊。

執照

此源代碼是根據此源樹的根​​目錄中的LICENSE文件中發現的MIT許可證的許可。