Source code for our paper "Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures"

Last update: Dec 10, 2022

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Overview

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

Code for the Multiplex Molecular Graph Neural Network (MXMNet) proposed in our paper, which has been accepted by the Machine Learning for Structural Biology Workshop (MLSB 2020) and the Machine Learning for Molecules Workshop (ML4Molecules 2020) at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020).

Overall Architecture

Requirements

CUDA : 10.1 Python : 3.7.10

The other dependencies can be installed with:

pip install -r requirements.txt

How to Run

You can directly download, preprocess the QM9 dataset and train the model with

python main.py

Optional arguments:

  --gpu             GPU number
  --seed            random seed
  --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
  --target          index of target (0~11) for prediction on QM9
  --cutoff          distance cutoff used in the global layer

The default model to be trained is the MXMNet (BS=128, d_g=5) by using '--batch_size=128 --cutoff=5.0'.

Cite

If you find this model and code are useful in your work, please cite our paper:

@inproceedings{zhang2020molecular,
  title     = {Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures},
  author    = {Zhang, Shuo and Liu, Yang and Xie, Lei},
  booktitle = {NeurIPS-W},
  year      = {2020}
}

Comments

Predicting other values from the QM9 dataset

Hello!

The QM9 dataset also contains information regarding the atomization energies of molecules. Can we predict those by simply adding to the targets list in main.py?

Also, can you please explain why you add 5 to the target values in [7,8,9,10]?

opened by PratyushAvi 6
Using torch geometric QM9 dataset with this repository
Hello

I'm trying to run this code using the torch geometric data source for QM9.

However, I encounter the following error in the forward function of MXMNet:

h = torch.index_select(self.embeddings, 0, x.long()) RuntimeError: Index is supposed to be an empty tensor or a vector

x is a Tensor of shape 556,11 that seem to contain integers.
opened by ekosman 4
QM9 dataset's difference to pytorch-geometric version

Dear authors, May I ask what is the difference between your qm9 dataset and pytorch geometric qm9 dataset? It seems that your come is coming from pytorch-geometric but maybe has some revision. Could please let me know the difference? Thank you!

opened by LingxiaoShawn 3
Local cutoff distance

Hello!

Thank you for providing the code.

I wonder what the default values for the local message passing are. The local cutoff distance is based on the distance between two atoms that share a bond, isn't it? It seems you updated some default parameters that influence the local message passing from 3.6179 to 5 a couple of weeks ago. Isn't now the denominator for the local cutoff distance == global cutoff distance, when using the default value for global cutoff distance of 5?

Thanks a lot for your help in advance!

opened by jangerit 2
numpy 1.20.0 and python 3.6.9

Hi, I just started to clone the project and start MXMNet architecture in my environment.

I wonder how you deal with python 3.6.9 and numpy 1.20.0 version conflict.

The current RDkit version relies on numpy 1.20.0, and numpy 1.20.0 does not support python 3.6.

https://numpy.org/doc/stable/release/1.20.0-notes.html

If you need further information, please let me know. I would be happy to update the info.

I am installing the packages in my virtual environment python=3.6.9 via Miniconda3.

opened by Joeycho 2
Multiple GPUs
Hi,

I'm trying to set up MXMNet to use multiple GPUs. I've changed

model = MXMNet(config).to(device)

to

model = MXMNet(config) model = nn.DataParallel(model) model.to(device)

However, this appears to be insufficient. Is there anything else or something else I need to do to use multiple GPUs?

Thank you!
opened by PratyushAvi 1
Example to add ESOL dataset

You give an interesting code and example, but I don't see how to use it with esol dataset.

I have seen that you combine pos and atom label in your data.x. but how do you build your edge_index ?

is it a local bonded index like in classical graphs or for full molecule indexes ?

Also does the Hs are needed for other target than QM9 ?

opened by thegodone 1
PDBbind

Hello, In the paper, pdbind is mentioned in the examples. I am not sure where can I find the benchmark in this repo. It is interesting to see how the non-bonded interactions between the ligand and the protein are treated.

opened by amrhamedp 1

Owner

shzhang

CS Ph.D. student. Interested in the Representation Learning on Graphs.

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