This is the official implementation for the paper:

Pre-training via Denoising for Molecular Property Prediction

by Sheheryar Zaidi*, Michael Schaarschmidt*, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez Gonzalez, Peter Battaglia, Razvan Pascanu, Jonathan Godwin.

Pre-training via denoising is a powerful representation learning technique for molecules. This repository contains an implementation of pre-training for the TorchMD-NET architecture, built off the original TorchMD-NET repository.

Clone the repository:

git clone https://github.com/shehzaidi/pre-training-via-denoising.git
cd pre-training-via-denoising

Create a virtual environment containing the dependencies and activate it:

conda env create -f environment.yml
conda activate pvd

Install the package into the environment:

pip install -e .

The model is pre-trained on the PCQM4Mv2 dataset, which contains over 3 million molecular structures at equilibrium. Run the following command to pre-train the architecture first. Note that this will download and pre-process the PCQM4Mv2 dataset when run for the first time, which can take a couple of hours depending on the machine.

python scripts/train.py --conf examples/ET-PCQM4MV2.yaml --layernorm-on-vec whitened --job-id pretraining

The option --layernorm-on-vec whitened includes an optional equivariant whitening-based layer norm, which stabilizes denoising. The pre-trained model checkpoint will be in ./experiments/pretraining. A pre-trained checkpoint is included in this repo at checkpoints/denoised-pcqm4mv2.ckpt.

To fine-tune the model for HOMO/LUMO prediction on QM9, run the following command, specifying homo/lumo and the path to the pre-trained checkpoint:

python scripts/train.py --conf examples/ET-QM9-FT.yaml --layernorm-on-vec whitened --job-id finetuning --dataset-arg <homo/lumo> --pretrained-model <path to checkpoint>

The fine-tuned model achieves state-of-the-art results for HOMO/LUMO on QM9:

By default, the code will use all available GPUs to train the model. We used three GPUs for pre-training and two GPUs for fine-tuning (NVIDIA RTX 2080Ti), which can be set by prefixing the commands above with e.g. CUDA_VISIBLE_DEVICES=0,1,2 to use three GPUs.

It is straightforward to implement denoising in an existing codebase. There are broadly three steps:

1. Add noise to the input molecular structures in the dataset. See here.
2. Add an output module to the architecture for predicting the noise. See here.
3. Use (or augment an existing loss with) an L2 loss for training the model. See here.

If you have found this work useful, please consider using the following citation:

@misc{zaidi2022pretraining,
      title={Pre-training via Denoising for Molecular Property Prediction}, 
      author={Sheheryar Zaidi and Michael Schaarschmidt and James Martens and Hyunjik Kim and Yee Whye Teh and Alvaro Sanchez-Gonzalez and Peter Battaglia and Razvan Pascanu and Jonathan Godwin},
      year={2022},
      eprint={2206.00133},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}