GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning

This is the official codebase of the paper GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning.

Minghao Xu, Yunteng Geng*, Yihang Zhang*, Ling Yang, Jian Tang, Wentao Zhang, (*equal contribution)

Overview

We build GlycanML, a comprehensive multi-task and multi-structure benchmark for Glycan Machine Learning. The GlycanML benchmark consists of diverse types of tasks including glycan taxonomy prediction, glycan immunogenicity prediction, glycosylation type prediction, and protein-glycan interaction prediction. Glycans can be represented by both sequences and graphs in GlycanML, which enables us to extensively evaluate sequence-based models and graph neural networks (GNNs) on benchmark tasks under both single-task learning and multi-task learning settings.

This codebase is based on PyTorch and TorchDrug, a powerful and flexible machine learning platform for drug modeling. It supports training and inference with multiple GPUs or multiple machines.

Installation

You may install the dependencies of TorchDrug as below. Generally, they work with Python 3.8/3.9 and PyTorch version >= 1.8.0.

conda create -n torchdrug python=3.9
conda activate torchdrug

conda install --yes pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch
conda install --yes pyg pytorch-scatter pytorch-cluster -c pyg
pip install torchdrug
pip install pyyaml easydict scipy fair-esm
pip install dill biopandas biopython e3nn wandb tensorboard tensorboardX
pip install glycowork[draw]

Model Training

Experimental Configurations

We provide a yaml based config for each benchmark experiment in our paper. The configs of all baselines for single-task and multi-task learning are stored in ./config/ with the following folder structure:

config
 └── single_task
     ├── CNN
     ├── ResNet
     ├── LSTM
     ├── BERT
     ├── GCN
     ├── RGCN
     ├── GAT
     ├── GIN
     ├── CompGCN
     ├── MPNN
 ├── multi_task
     ├── CNN
     ├── ResNet
     ├── LSTM
     ├── BERT
     ├── GCN
     ├── RGCN
     ├── GAT
     ├── GIN
     ├── CompGCN
     ├── MPNN

Single-Task Learning

Single-GPU. By setting gpus: [0], the experiment is performed under a single GPU. You can use the following command to run with seed 0:

python scripts/run_single.py --config ./configs/single_task/$model/$yaml_config \
    --gpus [0] --seed 0

Multi-GPU. By setting gpus: [0,1,2,3], the experiment is performed under 4 GPUs of a node. You can use the following command to run with seed 0:

python -m torch.distributed.launch --nproc_per_node=4 scripts/run_single.py \
    --config ./configs/single_task/$model/$yaml_config --gpus [0,1,2,3] --seed 0

Multi-Task Learning

Single-GPU. By setting gpus: [0], the experiment is performed under a single GPU. You can use the following command to run with seed 0:

python scripts/run_single.py --config ./configs/multi_task/$model/$yaml_config \
    --gpus [0] --seed 0

Multi-GPU. By setting gpus: [0,1,2,3], the experiment is performed under 4 GPUs of a node. You can use the following command to run with seed 0:

python -m torch.distributed.launch --nproc_per_node=4 scripts/run_single.py \
    --config ./configs/multi_task/$model/$yaml_config --gpus [0,1,2,3] --seed 0

Benchmark Results

At the website of GlycanML, we maintain a leaderboard for each benchmark task. In the future, we will open the entrance to receive new benchmark results of new methods from the community.

License

This codebase is released under the Apache License 2.0 as in the LICENSE file.

Citation

If you find this codebase helpful in your research, please cite the following paper.

@article{xu2024glycanml,
  title={GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning}, 
  author={Minghao Xu and Yunteng Geng and Yihang Zhang and Ling Yang and Jian Tang and Wentao Zhang},
  journal={arXiv preprint arXiv:2405.16206},
  year={2024},
}