YukumoHunter / e3-molecule-diffusion

Equivariant Diffusion for Molecule Generation in 3D

Harold Ruiter, Marina Orozco Gonzalez, Ricardo Chavez Torres, Robin Reitsma

The main objectives of our project are:

1. Do a thorough review of the current literature on geometric deep learning for molecule generation.

This review can be found in our blogpost. There, we summarize the key elements of Diffusion Models, Graphs and Equivariance, and how can they help molecules generation. Also, we make an introduction to JAX, a Python framework with first class support for high-performance, scalable computation.

2. Reproduce the results of Equivariant Diffusion Hoogeboom et al. (2022)

In order to do this, we ran the code from the original repository following the steps they indicated in their README.

3. Implement the Equivariant Diffusion Hoogeboom et al. (2022) algorithm in JAX.

This is the main topic of this repository and of the files you can find in src.

In order to run the training, the steps are the same as in the original repository:

• Training the EDM

python main_qm9.py --n_epochs 3000 --exp_name edm_qm9 --n_stability_samples 1000 --diffusion_noise_schedule polynomial_2 --diffusion_noise_precision 1e-5 --diffusion_steps 1000 --diffusion_loss_type l2 --batch_size 64 --nf 256 --n_layers 9 --lr 1e-4 --normalize_factors [1,4,10] --test_epochs 20 --ema_decay 0.9999

NOTE: the code will start running but the results aren't correct. Some debug still needs to be done.

4. Experiments

Even though we haven't finish our implementation in Jax yet (progess can be checked in src), we expect a significant improvement in training time by using JAX.

Structure

repo
.
|-- readme.md                  # Description of the repo with relevant getting started info
|-- blogpost.md                # Main deliverable: a blogpost style report
|-- src                        # Contains the main project files
|   |-- configs                # Configuration files
|   |   |..
|   |-- data                   # Data handling and preprocessing scripts
|   |   |..
|   |-- egnn                   # Equivariant Graph Neural Network implementation
|   |   |..
|   |-- equivariant_diffusion  # Diffusion model implementation
|   |   |..
|   |-- generated_samples      # Generated molecular samples
|   |   |..
|   |-- qm9                    # QM9 dataset handling
|   |   |..
|   |-- main_qm9.py                    # Main script for running the model
|   |-- ..

Getting Started

Prerequisites

Ensure you have the following software installed:

• Python 3.12.3 (lower versions might also work)
• Git

Setting Up the Environment

1. Clone this repo

git clone https://github.com/YukumoHunter/e3-molecule-diffusion
cd repo

2. Create and activate an environment:

python -m venv .venv
. .venv/bin/activate

3. Install dependencies: All dependencies are listed in the requirements.txt file. Install them using pip:

pip install -r requirements.txt

Usage

To run the main model on the QM9 dataset:

python src/main_qm9.py

Our blogpost can be fount at blogpost.md.