BALM

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Inference code for Bio-Inspired Antibody Language Model

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# Bio-Inspired Antibody Language Model (BALM)
This repository contains inference code for **Accurate Prediction of Antibody Function and Structure Using Bio-Inspired Antibody Language Model**
![arch-BALMFold](https://raw.githubusercontent.com/BEAM-Labs/BALM/main/docs/arch-BALMFold.jpg)

## Create Environment
```
conda env create -f environment.yml
conda activate BALM
```

## Preparation
The pre-trained weights of BALM can be downloaded from Google Drive link: [pretrained-BALM](https://drive.google.com/drive/folders/1foy264CIawBIT3QFTdc6JBVxw6MQfvQd?usp=sharing)

## Run inference

```
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 server
BALMFold is based on BALM to predict antibody tertiary structure with primary sequence. The online server is freely available at [BALMFold server](https://beamlab-sh.com/models/BALMFold). Just try it. :)

## Citation
If you find our model is useful for you, please cite as:

```
@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}
}
```

The architecture and pre-training process of BALM builds on the ESM and Hugging Face modeling framework. We really appreciate the work of [ESM](https://github.com/facebookresearch/esm) and [Hugging Face](https://huggingface.co/) team.

## License
This source code is licensed under the MIT license found in the `LICENSE` file in the root directory of this source tree.

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