# Physics-aware-Multiplex-GNN

**Repository Path**: ahlih_admin/Physics-aware-Multiplex-GNN

## Basic Information

- **Project Name**: Physics-aware-Multiplex-GNN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-11-22
- **Last Updated**: 2023-11-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


# PAMNet: A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/a-universal-framework-for-accurate-and/drug-discovery-on-qm9)](https://paperswithcode.com/sota/drug-discovery-on-qm9?p=a-universal-framework-for-accurate-and)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/a-universal-framework-for-accurate-and/protein-ligand-affinity-prediction-on-pdbbind)](https://paperswithcode.com/sota/protein-ligand-affinity-prediction-on-pdbbind?p=a-universal-framework-for-accurate-and)

Official implementation of **PAMNet** (Physics-aware Multiplex Graph Neural Network) in our paper **[A universal framework for accurate and efficient geometric deep learning of molecular systems](https://www.nature.com/articles/s41598-023-46382-8)** accepted by *Nature Scientific Reports* (doi: 10.1038/s41598-023-46382-8).

PAMNet is an improved version of [MXMNet](https://github.com/zetayue/MXMNet) and outperforms state-of-the-art baselines regarding both accuracy and efficiency in diverse tasks including **small molecule property prediction**, **RNA 3D structure prediction**, and **protein-ligand binding affinity prediction**.
<br/><br/>

This implementation is also applicable to:
1. Our preprint [Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes](https://arxiv.org/abs/2206.02789).
2. Our paper [Physics-aware Graph Neural Network for Accurate RNA 3D Structure Prediction](https://arxiv.org/abs/2210.16392) on [Machine Learning for Structural Biology Workshop](https://www.mlsb.io/) at *NeurIPS 2022*.

If you have any questions, feel free to open an issue or reach out to: szhang4@gradcenter.cuny.edu.

## Overall Architecture

<p align="center">
<img src="https://github.com/zetayue/Physics-aware-Multiplex-GNN/blob/main/overview.png?raw=true">
</p>

## Setup
### Environment

 - Python : 3.7.4 
 - CUDA : 10.1

**Optional**: Install Open Babel 3.1.1 for binding affinity prediction on PDBbind:

 1. Download [source file](https://anaconda.org/conda-forge/openbabel/3.1.1/download/linux-64/openbabel-3.1.1-py37h200e996_1.tar.bz2)
 2. `conda install filename`

The other dependencies can be installed with:
```
pip install -r requirements.txt
```
### Datasets

**QM9 for small molecule property prediction:**

  The training script (`main_qm9.py`) will automatically download the QM9 dataset and preprocess it.

**PDBbind for protein-ligand binding affinity prediction:**

 1. Download `PDBbind_dataset.tar.gz` from [dropbox](https://www.dropbox.com/sh/2uih3c6fq37qfli/AAD-LHXSWMLAuGWzcQLk5WI3a)
 2. Unzip the downloaded file under `./data/PDBbind`. There will be two subfolders (`core-set` and `refined-set`) after the unzip
 3. Run `python preprocess_pdbbind.py` to preprocess the dataset to construct graphs

**RNA-Puzzles for RNA 3D structure prediction:**

 1. Download `classics_train_val.tar` from [Stanford Digital Repository](https://doi.org/10.25740/bn398fc4306)
 2. Unzip the downloaded file under `./data/RNA-Puzzles`. There will be one subfolder `classics_train_val` containing `example_train` and `example_val`after the unzip
 3. Run `python preprocess_rna_puzzles.py` to preprocess the dataset to construct graphs

## How to Run
### Arguments
```
  --gpu             GPU number
  --seed            random seed
  --dataset         dataset to be used
  --epochs          number of epochs to train
  --lr              initial learning rate
  --wd              weight decay value
  --n_layer         number of hidden layers
  --dim             size of input hidden units
  --batch_size      batch size
  --cutoff_l        distance cutoff used in the local layer
  --cutoff_g        distance cutoff used in the global layer
  --model           model to be used on QM9
  --target          index of target (0~11) for prediction on QM9
```
### Example command for training and evaluation
**Small molecule property prediction on QM9:**

    python -u main_qm9.py --dataset 'QM9' --model 'PAMNet' --target=7 --epochs=900 --batch_size=32 --dim=128 --n_layer=6 --lr=1e-4

**Protein-ligand binding affinity prediction on PDBbind:**

    python -u main_pdbbind.py --dataset 'PDBbind' --epochs=170 --batch_size=32 --dim=128 --n_layer=3 --lr=1e-3

**RNA 3D structure prediction on RNA-Puzzles:**

    python -u main_rna_puzzles.py --dataset 'RNA-Puzzles' --epochs=15 --batch_size=8  --dim=16 --n_layer=1 --lr=1e-4

## Citation
If you find our model and code helpful in your work, please consider citing us:
```
@article{zhang2023universal,
  title={A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems},
  author={Zhang, Shuo and Liu, Yang and Xie, Lei},
  journal={Scientific Reports},
  volume={13},
  number={1},
  pages={19171},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@article{zhang2022physics,
  title={Physics-aware graph neural network for accurate RNA 3D structure prediction},
  author={Zhang, Shuo and Liu, Yang and Xie, Lei},
  journal={arXiv preprint arXiv:2210.16392},
  year={2022}
}
```