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:

Getting Started

Installation

Clone the github repo and set up conda environment

```

Clone the GitHub Repository

$ git clone

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.

1. 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.

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

3. load_model: Change this to the model name of your downloaded MolCLR model.

4. data_path: Specify the path to your dataset where the training data is stored.

$ 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 'infervis.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 `tasknameand modify thedatapathfield in theinfervis.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

bibtex @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

• MolCLR: https://github.com/yuyangw/MolCLR
• Mordred: https://github.com/mordred-descriptor/mordred