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AGILE

This is the official codebase for AGILE Platform: A Deep Learning-Powered Approach to Accelerate LNP Development for mRNA Delivery.

🥳 Updates: AGILE has been accepted to Nature Communications!

Introduction

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:

:test_tube: Efficient design and synthesis of combinatorial lipid libraries
:brain: Comprehensive in silico lipid screening employing deep neural networks
:dna: 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 :stopwatch:！

An overview of AGILE can be seen below:

<p align="center"> <img src="https://github.com/bowang-lab/AGILE/blob/590b980e55a4e43dff5f1bc8c86d2d02791be05e/figures/AGILE_overview.png" alt="AGILE architecture diagram" border="0"> </p>

Getting Started

Installation

Clone the github repo and set up conda environment

# Clone the GitHub Repository
$ git clone <this-repo-url>

# Create a new environment
$ conda create --name agile python=3.9 -y
$ conda activate agile

# Install PyTorch and torchvision with CUDA support. Make sure the versions are compatible with your CUDA version.
$ 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

Dataset

We have provided the fine-tuning library and candidate library used in the paper in data.zip, extract the zip file under ./data folder.

Pre-training

The pre-trained AGILE model on the 60k virtual lipid library can be found in ckpt/pretrained_agile_60k. If your data significantly differs from the 60k virtual lipid library, the pre-trained AGILE model might not perform optimally. In such cases, you can pre-train the model with your own dataset to potentially achieve better results.

Steps to Pre-train with Your Data:

  1. Obtain Pre-trained Base Models: Download the pre-trained MolCLR models, which serve as a starting point for further training. These models are available at here.

  2. Set Up the Model Directory: Place the downloaded MolCLR model files in the ./ckpt directory within your project folder. This ensures they are properly accessed by the training script.

  3. Configure Training Settings: Open the config_pretrain.yaml file and make the following adjustments:

$ python pretrain.py config_pretrain.yaml

Fine-tuning

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.

If you would like to fine-tune AGILE with your own dataset, create your own task_name in the config file, and modify the following fields in the finetune.py:

config["dataset"]["task"] = "regression"                                             # keep it the same
config["dataset"]["data_path"] = "data/finetuning_set_smiles_plus_features.csv"      # change it to the path of your own fine-tunning dataset
target_list = ["expt_Hela"]                                                          # change it to the column name of the regression labels
config["dataset"]["feature_cols"] = get_desc_cols(config["dataset"]["data_path"])    # keep it the same if you have additional features
config["model"]["pred_additional_feat_dim"] = len(config["dataset"]["feature_cols"]) # keep it the same if you have additional features

Then run:

$ python finetune.py config_finetune.yaml

The fine-tuned AGILE model will be stored in ./finetune.

Inference and visualization

To perform model inference with the fine-tuned AGILE model, you can run the following command:

$ python infer_vis.py <folder name of the fine-tuned model>

Note that the 'infer_vis.py' will pick up the config yaml file from the fine-tuned AGILE model folder. So the above command will perform model inference with the specified AGILE fine-tuned model on the fine-tuning dataset. To perform inference on new data, you will need to modify the config file with a new task_name and modify the data_path field in the infer_vis.py:

config["dataset"]["task"] = "regression"                                             # keep it the same
config["dataset"]["data_path"] = "data/candidate_set_smiles_plus_features.csv"       # change it to the path of your own inference dataset
target_list = ["desc_ABC/10"]                                                        # it will be the dummy label for visualization
config["dataset"]["feature_cols"] = get_desc_cols(config["dataset"]["data_path"])    # keep it the same if you have additional features
config["model"]["pred_additional_feat_dim"] = len(config["dataset"]["feature_cols"]) # keep it the same if you have additional features

The predicted output (in .csv file) and visualization plots (in .png files) will be stored in the same fine-tuned AGILE model folder.

Citing AGILE

@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}
}

Acknowledgement