infoalign 0.1.0

A package for the paper: learning molecular representation in a cell

Package for Learning Molecular Representation in a Cell

InfoAlign learns molecular representations from bottleneck information derived from molecular structures, cell morphology, and gene expressions. For more details, please refer to our paper.

Requirements

This project was developed and tested with the following versions:

• Python: 3.11.7
• PyTorch: 2.2.0+cu118
• Torch Geometric: 2.6.1

All dependencies are listed in the requirements.txt file.

Setup Instructions

1. Create a Conda Environment:

   conda create --name infoalign python=3.11.7
   

2. Activate the Environment:

   conda activate infoalign
   

3. Install Dependencies:

   pip install -r requirements.txt
   

Usage

Fine-tuning

We provide a pretrained checkpoint available for download from Hugging Face. For fine-tuning and inference, use the following commands. The pretrained model will be automatically downloaded to the ckpt/pretrain.pt file by default.

python main.py --model-path ckpt/pretrain.pt --dataset finetune-chembl2k
python main.py --model-path ckpt/pretrain.pt --dataset finetune-broad6k
python main.py --model-path ckpt/pretrain.pt --dataset finetune-biogenadme
python main.py --model-path ckpt/pretrain.pt --dataset finetune-moltoxcast

Alternatively, you can manually download the model weights and place the pretrain.pt file under the ckpt folder along with its corresponding YAML configuration file.

Note: If you wish to access the cell morphology and gene expression features in the ChEMBL2k and Broad6K datasets for baseline evaluation, visit our Hugging Face repository to download these features.

Pretraining

To pretrain the model from scratch, execute the following command:

python main.py --model-path "ckpt/pretrain.pt" --lr 1e-4 --wdecay 1e-8 --batch-size 3072

This will automatically download the pretraining dataset from Hugging Face. If you prefer to download the dataset manually, place all pretraining data files in the raw_data/pretrain/raw folder.

The pretrained model will be saved in the ckpt folder as pretrain.pt.

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Data source

For readers interested in data collection, here are the sources:

1. Cell Morphology Data

   • JUMP dataset: The data are from "JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations" and can be downloaded here. The dataset includes chemical and genetic perturbations for cell morphology features.
   • Bray's dataset: "A dataset of images and morphological profiles of 30,000 small-molecule treatments using the Cell Painting assay". Download from GigaDB. Processed version available on Zenodo.

2. Gene Expression Data

   • LINCS L1000 gene expression data from the paper "Drug-induced adverse events prediction with the LINCS L1000 data": Data.

3. Relationships

   • Gene-gene, gene-compound relationships from Hetionet: Data.

Citation

If you find this repository useful, please cite our paper:

@article{liu2024learning,
  title={Learning Molecular Representation in a Cell},
  author={Liu, Gang and Seal, Srijit and Arevalo, John and Liang, Zhenwen and Carpenter, Anne E and Jiang, Meng and Singh, Shantanu},
  journal={arXiv preprint arXiv:2406.12056},
  year={2024}
}

Acknowledgement

Template adapted from: https://github.com/lwaekfjlk/python-project-template. Thanks to the authors for their open-source contribution.