This is the official codebase for AGILE Platform: A Deep Learning-Powered Approach to Accelerate LNP Development for mRNA Delivery.[biorXiv]
AGILE (AI-Guided Ionizable Lipid Engineering) platform streamlines the iterative development of ionizable lipids, crucial components for LNP-mediated mRNA delivery. This platform brings forth three significant features:
🧪 Efficient design and synthesis of combinatorial lipid libraries
🧠 Comprehensive in silico lipid screening employing deep neural networks
🧬 Adaptability to diverse cell lines
It also significantly truncates the timeline for new ionizable lipid development, reducing it from potential months or even years to weeks ⏱️！
An overview of AGILE can be seen below:
Set up conda environment and clone the github repo
# create a new environment
$ conda create --name agile python=3.9 -y
$ conda activate agile
# install requirements
$ pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
$ pip install torch-geometric==2.2.0 torch-sparse==0.6.16 torch-scatter==2.1.0 -f https://data.pyg.org/whl/torch-1.12.0+cu113.html
$ pip install -r requirements.txt
The continuous pretraining of AGILE is inherited from MolCLR, the pre-trained MolCLR models can be found in link. To pre-train with your own data, you can modify the configurations in config_pretrain.yaml.
$ python pretrain.py config_pretrain.yaml
To fine-tune the AGILE pre-trained model for ionizable lipid prediction on the specific cell lines, you can modify the configurations in config_finetune.yaml. We have provided the pre-trained AGILE model on the 60k virtual lipid library, which can be found in ckpt/pretrained_agile_60k.
$ python finetune.py config_finetune.yaml
The current 'infer_vis.py' will perform model inference with the AGILE fine-tuned model on the fine-tuning dataset. To perform inference on new data, you can modify the config file to specify the new data path.
$ python infer_vis.py <folder name of the fine-tuned model>
@article{xu2023agile,
title={AGILE Platform: A Deep Learning-Powered Approach to Accelerate LNP Development for mRNA Delivery},
author={Xu, Yue and Ma, Shihao and Cui, Haotian and Chen, Jingan and Xu, Shufen and Wang, Kevin and Varley, Andrew and Lu, Rick Xing Ze and Bo, Wang and Li, Bowen},
journal={bioRxiv},
pages={2023--06},
year={2023},
publisher={Cold Spring Harbor Laboratory}
}