文件列表:

.dockerignore (329, 2023-08-10)
.env.example (346, 2023-08-10)
.pre-commit-config.yaml (3015, 2023-08-10)
Dockerfile (532, 2023-08-10)
LICENSE (1459, 2023-08-10)
Makefile (1169, 2023-08-10)
citation.bib (295, 2023-08-10)
configs (0, 2023-08-10)
configs\callbacks (0, 2023-08-10)
configs\callbacks\default.yaml (430, 2023-08-10)
configs\callbacks\early_stopping.yaml (1244, 2023-08-10)
configs\callbacks\ema.yaml (417, 2023-08-10)
configs\callbacks\model_checkpoint.yaml (1386, 2023-08-10)
configs\callbacks\model_summary.yaml (402, 2023-08-10)
configs\callbacks\none.yaml (0, 2023-08-10)
configs\callbacks\rich_progress_bar.yaml (289, 2023-08-10)
configs\datamodule (0, 2023-08-10)
configs\datamodule\dataloader_cfg (0, 2023-08-10)
configs\datamodule\dataloader_cfg\edm_geom_dataloader.yaml (454, 2023-08-10)
configs\datamodule\dataloader_cfg\edm_qm9_dataloader.yaml (446, 2023-08-10)
configs\datamodule\edm_geom.yaml (122, 2023-08-10)
configs\datamodule\edm_qm9.yaml (121, 2023-08-10)
configs\debug (0, 2023-08-10)
configs\debug\default.yaml (905, 2023-08-10)
configs\debug\fdr.yaml (125, 2023-08-10)
configs\debug\limit.yaml (223, 2023-08-10)
configs\debug\overfit.yaml (209, 2023-08-10)
configs\debug\profiler.yaml (182, 2023-08-10)
configs\experiment (0, 2023-08-10)
configs\experiment\geom_mol_gen_ddpm.yaml (3095, 2023-08-10)
configs\experiment\geom_mol_gen_ddpm_grid_search.yaml (3229, 2023-08-10)
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## Description A PyTorch hub of denoising diffusion probabilistic models designed to generate novel biological data ## How to run Install Mamba ```bash 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 ```bash # 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 . ``` Download data ```bash # fetch, extract, and clean-up preprocessed data wget https://zenodo.org/record/7881***1/files/EDM.tar.gz tar -xzf EDM.tar.gz rm EDM.tar.gz ``` Download checkpoints **Note**: Make sure to be located in the project's root directory beforehand (e.g., `~/bio-diffusion/`) ```bash # fetch and extract model checkpoints directory wget https://zenodo.org/record/7881***6/files/GCDM_Checkpoints.tar.gz tar -xzf GCDM_Checkpoints.tar.gz rm GCDM_Checkpoints.tar.gz ``` ## How to train Train model with default configuration ```bash # 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/](configs/experiment/) ```bash python src/train.py experiment=experiment_name.yaml ``` Train a model for *unconditional* small molecule generation with the QM9 dataset (**QM9**) ```bash python3 src/train.py experiment=qm9_mol_gen_ddpm.yaml ``` Train a model for *property-conditional* small molecule generation with the QM9 dataset (**QM9**) ```bash # 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] ``` Train a model for *unconditional* drug-size molecule generation with the GEOM-Drugs dataset (**GEOM**) ```bash python3 src/train.py experiment=geom_mol_gen_ddpm.yaml ``` **Note**: You can override any parameter from command line like this ```bash python src/train.py trainer.max_epochs=20 datamodule.dataloader_cfg.batch_size=*** ``` ## How to evaluate Reproduce our results for *unconditional* small molecule generation with the QM9 dataset ```bash 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" # note: `trainer.devices=[0]` selects the CUDA device available at index `0` - customize as needed using e.g., `nvidia-smi` python3 src/mol_gen_eval.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] ckpt_path="$qm9_model_1_ckpt_path" datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false num_samples=10000 sampling_batch_size=100 num_test_passes=5 python3 src/mol_gen_eval.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] ckpt_path="$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=[0] ckpt_path="$qm9_model_3_ckpt_path" datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false num_samples=10000 sampling_batch_size=100 num_test_passes=5 ``` Reproduce our results for *property-conditional* small molecule generation with the QM9 dataset ```bash qm9_alpha_generator_model_filepath="checkpoints/QM9/Conditional/alpha_model_epoch_1619-EMA.ckpt" qm9_gap_generator_model_filepath="checkpoints/QM9/Conditional/gap_model_epoch_1659-EMA.ckpt" qm9_homo_generator_model_filepath="checkpoints/QM9/Conditional/homo_model_epoch_1879-EMA.ckpt" qm9_lumo_generator_model_filepath="checkpoints/QM9/Conditional/lumo_model_epoch_1619-EMA.ckpt" qm9_mu_generator_model_filepath="checkpoints/QM9/Conditional/mu_model_epoch_1859-EMA.ckpt" qm9_Cv_generator_model_filepath="checkpoints/QM9/Conditional/Cv_model_epoch_1539-EMA" qm9_alpha_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_alpha" qm9_gap_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_gap" qm9_homo_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_homo" qm9_lumo_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_lumo" qm9_mu_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_mu" qm9_Cv_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_Cv" python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_alpha_generator_model_filepath" classifier_model_dir="$qm9_alpha_classifier_model_dir" property=alpha iterations=100 batch_size=100 python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_gap_generator_model_filepath" classifier_model_dir="$qm9_gap_classifier_model_dir" property=gap iterations=100 batch_size=100 python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_homo_generator_model_filepath" classifier_model_dir="$qm9_homo_classifier_model_dir" property=homo iterations=100 batch_size=100 python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_lumo_generator_model_filepath" classifier_model_dir="$qm9_lumo_classifier_model_dir" property=lumo iterations=100 batch_size=100 python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_mu_generator_model_filepath" classifier_model_dir="$qm9_mu_classifier_model_dir" property=mu iterations=100 batch_size=100 python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_Cv_generator_model_filepath" classifier_model_dir="$qm9_Cv_classifier_model_dir" property=Cv iterations=100 batch_size=100 ``` Reproduce our results for *property-specific* small molecule optimization with the QM9 dataset ```bash qm9_unconditional_generator_model_filepath="checkpoints/QM9/Unconditional/model_1_epoch_979-EMA.ckpt" qm9_alpha_conditional_generator_model_filepath="checkpoints/QM9/Conditional/alpha_model_epoch_1619-EMA.ckpt" qm9_gap_conditional_generator_model_filepath="checkpoints/QM9/Conditional/gap_model_epoch_1659-EMA.ckpt" qm9_homo_conditional_generator_model_filepath="checkpoints/QM9/Conditional/homo_model_epoch_1879-EMA.ckpt" qm9_lumo_conditional_generator_model_filepath="checkpoints/QM9/Conditional/lumo_model_epoch_1619-EMA.ckpt" qm9_mu_conditional_generator_model_filepath="checkpoints/QM9/Conditional/mu_model_epoch_1859-EMA.ckpt" qm9_Cv_conditional_generator_model_filepath="checkpoints/QM9/Conditional/Cv_model_epoch_1539-EMA.ckpt" qm9_alpha_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_alpha" qm9_gap_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_gap" qm9_homo_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_homo" qm9_lumo_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_lumo" qm9_mu_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_mu" qm9_Cv_classifier_model_dir="checkpoints/QM9/Property_Classifiers/exp_class_Cv" # unconditionally generate a batch of samples to property-optimize python3 src/mol_gen_eval_optimization_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false unconditional_generator_model_filepath="$qm9_unconditional_generator_model_filepath" conditional_generator_model_filepath="$qm9_alpha_conditional_generator_model_filepath" classifier_model_dir="$qm9_alpha_classifier_model_dir" num_samples=1000 sampling_output_dir="./optim_mols/" property=alpha iterations=10 num_optimization_timesteps=100 return_frames=1 generate_molecules_only=true use_pregenerated_molecules=false # optimize generated samples for specific molecular properties python3 src/mol_gen_eval_optimization_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false unconditional_generator_model_filepath="$qm9_unconditional_generator_model_filepath" conditional_generator_model_filepath="$qm9_alpha_conditional_generator_model_filepath" classifier_model_dir="$qm9_alpha_classifier_model_dir" num_samples=1000 sampling_output_dir="./optim_mols/" property=alpha iterations=10 num_optimization_timesteps=100 return_frames=1 generate_molecules_only=false use_pregenerated_molecules=true python3 src/mol_gen_eval_optimization_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false unconditional_generator_model_filepath="$qm9_unconditional_generator_model_filepath" conditional_generator_model_filepath="$qm9_gap_conditional_generator_model_filepath" classifier_model_dir="$qm9_gap_classifier_model_dir" num_samples=1000 sampling_output_dir="./optim_mols/" property=gap iterations=10 num_optimization_timesteps=100 return_frames=1 generate_molecules_only=false use_pregenerated_molecules=true python3 src/mol_gen_eval_optimization_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false unconditional_generator_model_filepath="$qm9_unconditional_generator_model_filepath" conditional_generator_model_filepath="$qm9_homo_conditional_generator_model_filepath" classifier_model_dir="$qm9_homo_classifier_model_dir" num_samples=1000 sampling_output_dir="./optim_mols/" property=homo iterations=10 num_optimization_timesteps=100 return_frames=1 generate_molecules_only=false use_pregenerated_molecules=true python3 src/mol_gen_eval_optimization_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false unconditional_generator_model_filepath="$qm9_unconditional_generator_model_filepath" conditional_generator_model_filepath="$qm9_lumo_conditional_generator_model_filepath" classifier_model_dir="$qm9_lumo_classifier_model_dir" num_samples=1000 sampling_output_dir="./optim_mols/" property=lumo iterations=10 num_optimization_timesteps=100 return_frames=1 generate_molecules_only=false use_pregenerated_molecules=true python3 src/mol_gen_eval_optimization_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false unconditional_generator_model_filepath="$qm9_unconditional_generator_model_filepath" conditional_generator_model_filepath="$qm9_mu_conditional_generator_model_filepath" classifier_model_dir="$qm9_mu_classifier_model_dir" num_samples=1000 sampling_output_dir="./optim_mols/" property=mu iterations=10 num_optimization_timesteps=100 return_frames=1 generate_molecules_only=false use_pregenerated_molecules=true python3 src/mol_gen_eval_optimization_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false unconditional_generator_model_filepath="$qm9_unconditional_generator_model_filepath" conditional_generator_model_filepath="$qm9_Cv_conditional_generator_model_filepath" classifier_model_dir="$qm9_Cv_classifier_model_dir" num_samples=1000 sampling_output_dir="./optim_mols/" property=Cv iterations=10 num_optimization_timesteps=100 return_frames=1 generate_molecules_only=false use_pregenerated_molecules=true ``` Reproduce our results for *unconditional* drug-size molecule generation with the GEOM-Drugs dataset ```bash geom_model_1_ckpt_path="checkpoints/GEOM/Unconditional/model_1_epoch_76-EMA.ckpt" geom_model_2_ckpt_path="checkpoints/GEOM/Unconditional/model_2_epoch_79-EMA.ckpt" geom_model_3_ckpt_path="checkpoints/GEOM/Unconditional/model_3_epoch_78-EMA.ckpt" python3 src/mol_gen_eval.py datamodule=edm_geom model=geom_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] ckpt_path="$geom_model_1_ckpt_path" datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false num_samples=10000 sampling_batch_size=100 num_test_passes=5 python3 src/mol_gen_eval.py datamodule=edm_geom model=geom_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] ckpt_path="$geom_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_geom model=geom_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] ckpt_path="$geom_model_3_ckpt_path" datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false num_samples=10000 sampling_batch_size=100 num_test_passes=5 ``` ## How to sample *Unconditionally* generate small molecules similar to those contained within the QM9 dataset ```bash qm9_model_ckpt_path="checkpoints/QM9/Unconditional/model_1_epoch_979-EMA.ckpt" output_dir="./" my_seed_value=123 python3 src/mol_gen_sample.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] ckpt_path="$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="$output_dir" seed="$my_seed_value" ``` *Property-conditionally* generate small molecules similar to those contained within the QM9 dataset ```bash 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" qm9_Cv_model_ckpt_path="checkpoints/QM9/Conditional/Cv_model_epoch_1539-EMA.ckpt" output_dir="./" my_seed_value=123 python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] 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" seed="$my_seed_value" python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_gap_model_ckpt_path" property=gap iterations=100 batch_size=100 sweep_property_values=true num_sweeps=10 output_dir="$output_dir" seed="$my_seed_value" python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] 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" seed="$my_seed_value" python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_lumo_model_ckpt_path" property=lumo iterations=100 batch_size=100 sweep_property_values=true num_sweeps=10 output_dir="$output_dir" seed="$my_seed_value" python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] 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" seed="$my_seed_value" python3 src/mol_gen_eval_conditional_qm9.py datamodule=edm_qm9 model=qm9_mol_gen_ddpm logger=csv trainer.accelerator=gpu trainer.devices=[0] datamodule.dataloader_cfg.num_workers=1 model.diffusion_cfg.sample_during_training=false generator_model_filepath="$qm9_Cv_model_ckpt_path" property=Cv iterations=100 batch_size=100 sweep_property_values=true num_sweeps=10 output_dir="$output_dir" seed="$my_seed_value" ``` *Unconditionally* generate drug-size molecules similar to those contained within the GEOM-Drugs dataset ```bash geom_model_ckpt_path="checkpoints/GEOM/Unconditional/model_2_epoch_79-EMA.ckpt" output_dir="./" my_se ... 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