AIDO.Protein-16B-v1

AIDO.Protein-16B-v1 continues the pre-training of AIDO.Protein-16B using an additional 100 billion amino acids from Uniref90.

How to Use

Build any downstream models from this backbone with ModelGenerator

For more information, visit: Model Generator

mgen fit --model SequenceClassification --model.backbone aido_protein_16b_v1 --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>
mgen test --model SequenceClassification --model.backbone aido_protein_16b_v1 --data SequenceClassificationDataModule --data.path <hf_or_local_path_to_your_dataset>

Or use directly in Python

Embedding

from modelgenerator.tasks import Embed
model = Embed.from_config({"model.backbone": "aido_protein_16b_v1"}).eval()
transformed_batch = model.transform({"sequences": ["HELLQ", "WRLD"]})
embedding = model(transformed_batch)
print(embedding.shape)
print(embedding)

Sequence Level Classification

import torch
from modelgenerator.tasks import SequenceClassification
model = SequenceClassification.from_config({"model.backbone": "aido_protein_16b_v1", "model.n_classes": 2}).eval()
transformed_batch = model.transform({"sequences": ["HELLQ", "WRLD"]})
logits = model(transformed_batch)
print(logits)
print(torch.argmax(logits, dim=-1))

Token Level Classification

import torch
from modelgenerator.tasks import TokenClassification
model = TokenClassification.from_config({"model.backbone": "aido_protein_16b_v1", "model.n_classes": 3}).eval()
transformed_batch = model.transform({"sequences": ["HELLQ", "WRLD"]})
logits = model(transformed_batch)
print(logits)
print(torch.argmax(logits, dim=-1))

Regression

from modelgenerator.tasks import SequenceRegression
model = SequenceRegression.from_config({"model.backbone": "aido_protein_16b_v1"}).eval()
transformed_batch = model.transform({"sequences": ["HELLQ", "WRLD"]})
logits = model(transformed_batch)
print(logits)

Citation

Please cite AIDO.Protein using the following BibTex code:

@inproceedings{sun_mixture_2024,
    title = {Mixture of Experts Enable Efficient and Effective Protein Understanding and Design},
    url = {https://www.biorxiv.org/content/10.1101/2024.11.29.625425v1},
    doi = {10.1101/2024.11.29.625425},
    publisher = {bioRxiv},
    author = {Sun, Ning and Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Cheng, Xingyi and Song, Le and Xing, Eric P.},
    year = {2024},
    booktitle={NeurIPS 2024 Workshop on AI for New Drug Modalities},
}