license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- biology
- text-generation-inference
- aptamer
AptaGPT
AptaGPT is a generative pre-trained language model for aptamer design. The model focuses on the generation of a new sequence space of aptamers, trained and fine-tuned using the third and sixth round of SELEX data for B cell maturation antigen (BCMA).
Dataset
AptaGPT was pre-trained using a large dataset consisting of 108,229,900 sequences from the third round of the SELEX process targeting BCMA. This extensive dataset provided a robust foundation for learning generalized patterns in aptamer sequences. For fine-tuning, the model utilized 9,350 sequences from the sixth round of SELEX. All aptamer sequences used for both pre-training and fine-tuning are 35 nucleotides in length.
Requirements
Before running the AptaGPT model, the following Python dependencies need to be installed:
pip install transformers sentencepiece
Usage Examples
To load the model form hugging face:
from transformers import pipeline
aptagpt = pipeline('text-generation', model="tmbj-aidd/aptagpt-bcma")
To generate aptamer sequences:
sequences = aptagpt("<|endoftext|>",
max_length=15,
do_sample=True,
top_k=700,
repetition_penalty=1.2,
num_return_sequences=10,
)
print(sequences)