Bio-Diffusion

Description

A PyTorch hub of denoising diffusion probabilistic models designed to generate novel biological data

How to run

Install Mamba

wget "https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-$(uname)-$(uname -m).sh"
bash Mambaforge-$(uname)-$(uname -m).sh  # accept all terms and install to the default location
rm Mambaforge-$(uname)-$(uname -m).sh  # (optionally) remove installer after using it
source ~/.bashrc  # alternatively, one can restart their shell session to achieve the same result

Install dependencies

# clone project
git clone https://github.com/BioinfoMachineLearning/bio-diffusion
cd bio-diffusion

# create conda environment
mamba env create -f environment.yaml
conda activate bio-diffusion  # note: one still needs to use `conda` to (de)activate environments

# install local project as package
pip3 install -e .

Add or upgrade dependencies

# when installing a new package with pip or conda
# e.g., pip3 install .....

# update master configuration of environment layout
mamba env export | head -n -1 > environment.yaml

# also, be sure to remove the line `- bio-diffusion==0.0.1` from the list of `pip` dependencies generated

# push environment changes to remote for others to see
git add environment.yaml && git commit -m "Update Conda environment" && git push origin main

# then, others can update their local environments as follows
git pull origin main
mamba env update -f environment.yaml

Download data

# initialize data directory structure
mkdir -p data

# fetch, extract, and clean-up preprocessed data
cd data/
wget https://zenodo.org/record/7542177/files/EDM.tar.gz
tar -xzf EDM.tar.gz
rm EDM.tar.gz
cd ../

Download checkpoints

Note: Make sure to be located in the project's root directory beforehand (e.g., ~/bio-diffusion/)

# fetch and extract model checkpoints directory
wget https://zenodo.org/record/7647653/files/GCDM_Checkpoints.tar.gz
tar -xzf GCDM_Checkpoints.tar.gz
rm GCDM_Checkpoints.tar.gz

How to train

Train model with default configuration

# train on CPU
python src/train.py trainer=cpu

# train on GPU
python src/train.py trainer=gpu

Train model with chosen experiment configuration from configs/experiment/

python src/train.py experiment=experiment_name.yaml

Train a model for unconditional small molecule generation with the QM9 dataset (QM9)

python3 src/train.py experiment=qm9_mol_gen_ddpm.yaml

Train a model for property-conditional small molecule generation with the QM9 dataset (QM9)

# choose a value for `model.module_cfg.conditioning` from the properties `[alpha, gap, homo, lumo, mu, Cv]`
python3 src/train.py experiment=qm9_mol_gen_conditional_ddpm.yaml model.module_cfg.conditioning=[alpha]

Note: You can override any parameter from command line like this

python src/train.py trainer.max_epochs=20 datamodule.dataloader_cfg.batch_size=64

How to evaluate

Reproduce our results for unconditional small molecule generation with the QM9 dataset

qm9_model_1_ckpt_path="checkpoints/QM9/Unconditional/model_1_epoch_979-EMA.ckpt"
qm9_model_2_ckpt_path="checkpoints/QM9/Unconditional/model_2_epoch_979-EMA.ckpt"
qm9_model_3_ckpt_path="checkpoints/QM9/Unconditional/model_3_epoch_1099-EMA.ckpt"

python3 src/mol_gen_eval.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 ckpt_path="$qm9_model_1_ckpt_path" datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false num_samples=10000 sampling_batch_size=100 num_test_passes=5
python3 src/mol_gen_eval.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 ckpt_path="$qm9_model_2_ckpt_path" datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false num_samples=10000 sampling_batch_size=100 num_test_passes=5
python3 src/mol_gen_eval.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 ckpt_path="$qm9_model_3_ckpt_path" datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false num_samples=10000 sampling_batch_size=100 num_test_passes=5

Reproduce our results for property-conditional small molecule generation with the QM9 dataset

qm9_alpha_generator_model_filepath="checkpoints/QM9/Conditional/alpha_model_epoch_1619-EMA.ckpt"
qm9_gap_generator_model_filepath="checkpoints/QM9/Conditional/gap_model_epoch_1659-EMA.ckpt"
qm9_homo_generator_model_filepath="checkpoints/QM9/Conditional/homo_model_epoch_1879-EMA.ckpt"
qm9_lumo_generator_model_filepath="checkpoints/QM9/Conditional/lumo_model_epoch_1619-EMA.ckpt"
qm9_mu_generator_model_filepath="checkpoints/QM9/Conditional/mu_model_epoch_1859-EMA.ckpt"

qm9_alpha_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_alpha"
qm9_gap_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_gap"
qm9_homo_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_homo"
qm9_lumo_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_lumo"
qm9_mu_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_mu"

python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_alpha_generator_model_filepath" classifier_model_dir="$qm9_alpha_classifier_model_dir" property=alpha iterations=100 batch_size=100
python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_gap_generator_model_filepath" classifier_model_dir="$qm9_gap_classifier_model_dir" property=gap iterations=100 batch_size=100
python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_homo_generator_model_filepath" classifier_model_dir="$qm9_homo_classifier_model_dir" property=homo iterations=100 batch_size=100
python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_lumo_generator_model_filepath" classifier_model_dir="$qm9_lumo_classifier_model_dir" property=lumo iterations=100 batch_size=100
python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_mu_generator_model_filepath" classifier_model_dir="$qm9_mu_classifier_model_dir" property=mu iterations=100 batch_size=100

How to sample

Unconditionally generate small molecules similar to those contained within the QM9 dataset

qm9_model_ckpt_path="checkpoints/QM9/Unconditional/model_1_epoch_979-EMA.ckpt"
output_dir="./"

python3 src/mol_gen_sample.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 ckpt_path="$qm9_model_ckpt_path" num_samples=250 num_nodes=19 all_frags=true sanitize=false relax=false num_resamplings=1 jump_length=1 num_timesteps=1000 output_dir="$output_dir"

Property-conditionally generate small molecules similar to those contained within the QM9 dataset

qm9_alpha_model_ckpt_path="checkpoints/QM9/Conditional/alpha_model_epoch_1619-EMA.ckpt"
qm9_gap_model_ckpt_path="checkpoints/QM9/Conditional/gap_model_epoch_1659-EMA.ckpt"
qm9_homo_model_ckpt_path="checkpoints/QM9/Conditional/homo_model_epoch_1879-EMA.ckpt"
qm9_lumo_model_ckpt_path="checkpoints/QM9/Conditional/lumo_model_epoch_1619-EMA.ckpt"
qm9_mu_model_ckpt_path="checkpoints/QM9/Conditional/mu_model_epoch_1859-EMA.ckpt"
output_dir="./"

python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_alpha_model_ckpt_path" property=alpha iterations=100 batch_size=100 sweep_property_values=true num_sweeps=10 output_dir="$output_dir"
python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_gap_model_ckpt_path" property=gap iterations=100 batch_size=100 sweep_property_values=true num_sweeps=10 output_dir="$output_dir"
python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_homo_model_ckpt_path" property=homo iterations=100 batch_size=100 sweep_property_values=true num_sweeps=10 output_dir="$output_dir"
python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_lumo_model_ckpt_path" property=lumo iterations=100 batch_size=100 sweep_property_values=true num_sweeps=10 output_dir="$output_dir"
python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=1 datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_mu_model_ckpt_path" property=mu iterations=100 batch_size=100 sweep_property_values=true num_sweeps=10 output_dir="$output_dir"

Acknowledgements

Bio-Diffusion builds upon the source code and data from the following projects:

We thank all their contributors and maintainers!

Citing this work

If you use the code or data associated with this package or otherwise find this work useful, please cite:

@article{morehead2023geometry,
  title={Geometry-Complete Diffusion for 3D Molecule Generation},
  author={Morehead, Alex and Cheng, Jianlin},
  journal={arXiv preprint arXiv:2302.04313},
  year={2023}
}

wconnell/bio-diffusion