MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation

Update (July 2024): My drive account has unfortunately been deleted, and I have lost access some checkpoints. If you happen to have a downloaded checkpoint stored locally, I would be glad if you could send me an email at vignac.clement@gmail.com or raise a Github issue.

Link to the paper

Clément Vignac*, Nagham Osman*, Laura Toni, Pascal Frossard

ECML 2023

Installation

This code was tested with PyTorch 2.0.1, cuda 11.8 and torch_geometric 2.3.1 on multiple gpus.

• Download anaconda/miniconda if needed

• Create a rdkit environment that directly contains rdkit:

  conda create -c conda-forge -n midi rdkit=2023.03.2 python=3.9

• conda activate midi

• Check that this line does not return an error:

  python3 -c 'from rdkit import Chem'

• Install the nvcc drivers for your cuda version. For example:

  conda install -c "nvidia/label/cuda-11.8.0" cuda

• Install a corresponding version of pytorch, for example:

  pip3 install torch==2.0.1 --index-url https://download.pytorch.org/whl/cu118

• Install other packages using the requirement file:

  pip install -r requirements.txt

• Run:

  pip install -e .

Datasets

• QM9 should download automatically
• For GEOM, download the data and put in MiDi/data/geom/raw/:
  • train: https://bits.csb.pitt.edu/files/geom_raw/train_data.pickle
  • validation: https://bits.csb.pitt.edu/files/geom_raw/val_data.pickle
  • test: https://bits.csb.pitt.edu/files/geom_raw/test_data.pickle

Thanks to Ian Dunn for the new links to the data!

Training:

First move inside the src folder (so that the outputs are saved at the right location):

Some examples:

QM9 without hydrogens on cpu

python3 main.py dataset=qm9 dataset.remove_h=True +experiment=qm9_no_h_uniform

GEOM-DRUGS with hydrogens on 2 gpus

python3 main.py dataset=geom dataset.remove_h=False +experiment=geom_with_h_uniform general.gpus=2

Resuming a previous run

First, retrieve the absolute path of the checkpoint, it looks like ABS_PATH=/home/vignac/MiDi/outputs/2023-02-13/18-10-49-geomH/checkpoints/geomH_bigger/epoch=219.ckpt'

Then run:

python3 main.py dataset=qm9 dataset.remove_h=True +experiment=qm9_no_h_uniform general.resume='ABS_PATH'

Evaluation

Sampling on multiple gpu is not really handled, we recommand sampling on a single gpu.

Run:

python3 main.py dataset=qm9 dataset.remove_h=True +experiment=qm9_no_h_uniform general.test_only='ABS_PATH'

Checkpoints

QM9 implicit H:

• command: python3 main.py dataset=qm9 dataset.remove_h=True +experiment=qm9_with_h_uniform
• checkpoint: missing

QM9 explicit H:

• command: python3 main.py dataset=qm9 dataset.remove_h=False +experiment=qm9_with_h_adaptive
• checkpoint: https://drive.google.com/file/d/1ij8e6Iz-JohCYcEuYVOw1J_GTtU7-JeO/view?usp=sharing Geom implicit H:
• command: python3 main.py dataset=geom dataset.remove_h=True +experiment=geom_no_h_uniform
• checkpoint: https://drive.google.com/file/d/13FCl8UE5sD_8fMkHLBAQNby0ZRtXa2QY/view?usp=sharing

Geom explicit H:

• Uniform:
  • command: python3 main.py dataset=geom dataset.remove_h=False +experiment=geom_with_h_uniform
  • checkpoint: https://drive.google.com/file/d/1oDL43KwX2JreJd7ib7QP71bXRpIePUF3/view?usp=sharing
• Adaptive:
  • command: python3 main.py dataset=geom dataset.remove_h=False +experiment=geom_with_h_adaptive
  • checkpoint: https://drive.google.com/file/d/1lOZseprglsJqtqenl7F9T124otHLeZks/view?usp=sharing

Generated samples

QM9 implicit H:

• Full graphs: ~~https://drive.switch.ch/index.php/s/dNFcouhBoqZfSjB~~
• Smiles: ~~https://drive.switch.ch/index.php/s/qrqhtFqLqOI17zo~~

QM9 explicit H:

• Full graphs: ~~https://drive.switch.ch/index.php/s/b3ffvPAw8CqgYym~~
• Smiles: ~~https://drive.switch.ch/index.php/s/OrhJb3s0rYlYUrS~~

Geom with explicit H:

• Full graphs: ~~https://drive.switch.ch/index.php/s/rzidWbKSz1qzfEu~~
• Smiles: ~~https://drive.switch.ch/index.php/s/TFx1D7OncQ5xAZq~~

Evaluate your model on the proposed metrics

To benchmark your own model with the proposed metrics, you can use the sampling_metrics function in src/metrics/molecular_metrics.py: sampling_metrics(molecules=molecule_list, name='my_method', current_epoch=-1, local_rank=0).

You'll need to write a few lines to load your generated graphs and create a list of Molecule objects (in src/analysis/rdkit_functions.py).

Use MiDi on a new dataset

To implement a new dataset, you will need to create a new file in the src/datasets folder. This file should implement a Dataset class, a Datamodule class and and Infos class. Check qm9_dataset.py and geom_dataset.py for examples.

Once the dataset file is written, the code in main.py can be adapted to handle the new dataset, and a new file can be added in configs/dataset.

Use OpenBabel for baseline results

• In this work, we use Open Babel GUI for bond prediction.
• Install OpenBabel that corresponds to the machine you have. You can download it using the following link.
• For the input format, you need to choose "xyz -- XYZ cartesian coordinates format".
• For the output format, you need to choose "sdf -- MDL MOL format".
• In the additional instructi ons window, write the word "end" in the section "Add or replace molecule title".
• Choose all the xyz files you want to do the bond prediction for in the input section
• Choose the directory where you want to save the output file, then click on Convert.
• You can then use the function open_babel_eval in midi/analysis/baselines_evaluation which requires the path as argument.

Cite this paper

@article{vignac2023midi,
  title={MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation},
  author={Vignac, Clement and Osman, Nagham and Toni, Laura and Frossard, Pascal},
  journal={arXiv preprint arXiv:2302.09048},
  year={2023}
}