FlowSite and HarmonicFlow

Paper on arXiv

Code of FlowSite and HarmonicFlow. HarmonicFlow generates binding structures for single ligands or "multi-ligands" (multiple small molecules and ions bound to the same pocket). FlowSite builds on HarmonicFlow and generates residues types for binding sites to bind a specific (multi-)ligand.

For using FlowSite to design your own binding sites: I am currently writing a better user interface and training a more "production ready" model on a mixture of pdbbind and Binding MOAD after pretraining with fake ligands on all of PDB and using backbone noise.
If you want, you can put your email here and I will notify you once that is done: https://forms.gle/KzfiaoLij6YHPy6R8

Feel free to reach out about any questions! hstark@mit.edu

FlowSite generative process: HarmonicFlow multi-ligand structure generation gif:

Setup Environment

We will set up the environment using Anaconda. This is an example for how to set up a working conda environment to run the code. Make sure that the pytorch and pytorch-geometric versions you use are compatible with each other. Also, if you do not use a GPU, rather use cpu instead of cu121 in the last line (also make sure that you use the correct cuda version if you do have a gpu).

conda create -c conda-forge -n flowsite rdkit python
pip install torch torchvision torchaudio
pip install torch_geometric
pip install pyyaml wandb biopython spyrmsd einops biopandas plotly prody tqdm lightning imageio
pip install e3nn

pip install torch_scatter torch_sparse torch_cluster -f https://data.pyg.org/whl/torch-2.1.0+cu121.html

Alternatively there is also an environment.yml file that you can use with conda env create -f environment.yml.

Design your Binding sites

Also feel free to reach out about any feedback, ideas or what you would like to UI to be! hstark@mit.edu

Retrain FlowSite/HarmonicFlow or run trained model on test set

Dataset

The files in index contain the names for the sequence and time splits of pdbbind and moad.

To obtain the pdbbind data:

download it from zenodo
unzip the directory and place it into data such that you have the path data/PDBBind_processed

To obtain the binding MOAD data:

download it from here
unzip the directory and place it into data such that you have the path data/moad/BindingMOAD_2020
run python moad_unit2pdb.py

Running on the test set with trained models

Run on the test set using trained model weights like this:

CUDA_VISIBLE_DEVICES="0" python -m train --run_name test_HarmonicFlow_seqSimSplit_RadPock --wandb --run_test --checkpoint trained_weights/0np1y80l/best.ckpt --batch_size 4 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --diffdock_pocket --tfn_use_aa_identities --self_condition_x 

CUDA_VISIBLE_DEVICES="0" python -m train --run_name test_FlowSite_pdbbind_seqSimSplit --wandb --run_test --checkpoint trained_weights/0np1y80l/best.ckpt --batch_size 16 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --pocket_residue_cutoff 12 --clamp_loss 10 --epochs 150 --num_inference 5 --layer_norm --gradient_clip_val 1 --save_inference --check_nan_grads --fake_constant_dur 100000 --fake_decay_dur 10 --fake_ratio_start 0.2 --fake_ratio_end 0.2 --residue_loss_weight 0.2 --use_tfn --use_inv --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --num_angle_pred 5 --ns 32 --nv 8 --batch_norm --self_condition_x --self_condition_inv --no_tfn_self_condition_inv --standard_style_self_condition_inv --self_fancy_init

Retrain HarmonicFlow and FlowSite

If training stopped for some reason you can restart by using --checkpoint wandbProjectName/IDofTheRun/last.ckpt.

FlowSite PDBBind and Binding MOAD:

CUDA_VISIBLE_DEVICES="0" python -m train --run_name FlowSite_pdbbind --wandb --batch_size 16 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --pocket_residue_cutoff 12 --clamp_loss 10 --epochs 150 --num_inference 5 --layer_norm --gradient_clip_val 1 --save_inference --check_nan_grads --fake_constant_dur 100000 --fake_decay_dur 10 --fake_ratio_start 0.2 --fake_ratio_end 0.2 --residue_loss_weight 0.2 --use_tfn --use_inv --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --num_angle_pred 5 --ns 32 --nv 8 --batch_norm --self_condition_x --self_condition_inv --no_tfn_self_condition_inv --standard_style_self_condition_inv --self_fancy_init

CUDA_VISIBLE_DEVICES="0" python -m train --run_name FlowSite_moad --wandb --batch_size 16 --train_split_path index/moad_mmseqs_30sim_train --val_split_path index/moad_mmseqs_30sim_val --predict_split_path index/moad_mmseqs_30sim_test --data_dir data/moad/moad_prepared --data_source moad --biounit1_only --pocket_residue_cutoff 12 --clamp_loss 10 --epochs 150 --num_inference 5 --layer_norm --gradient_clip_val 1 --save_inference --check_nan_grads --fake_constant_dur 100000 --fake_decay_dur 10 --fake_ratio_start 0.2 --fake_ratio_end 0.2 --residue_loss_weight 0.2 --use_tfn --use_inv --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --num_angle_pred 5 --ns 32 --nv 8 --batch_norm --self_condition_x --self_condition_inv --min_num_contacts 2 --no_tfn_self_condition_inv --self_fancy_init --correct_moad_lig_selection --check_val_every_n_epoch 3 --standard_style_self_condition_inv

HarmonicFlow on PDBBind with residue identities:

CUDA_VISIBLE_DEVICES="0" python -m train --run_name HarmonicFlow_pdbbind_seqSimSplit_radPock --wandb --batch_size 4 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --diffdock_pocket --tfn_use_aa_identities --self_condition_x

CUDA_VISIBLE_DEVICES="0" python -m train --run_name HarmonicFlow_pdbbind_seqSimSplit_distPock --wandb --batch_size 4 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x

CUDA_VISIBLE_DEVICES="0" python -m train --run_name HarmonicFlow_pdbbind_timesplit_distPock --wandb --batch_size 4 --train_split_path index/timesplit_no_lig_overlap_train --val_split_path index/timesplit_no_lig_overlap_val --predict_split_path index/timesplit_test --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 5 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x

CUDA_VISIBLE_DEVICES="0" python -m train --run_name HarmonicFlow_pdbbind_timesplit_radPock --wandb --batch_size 4 --train_split_path index/timesplit_no_lig_overlap_train --val_split_path index/timesplit_no_lig_overlap_val --predict_split_path index/timesplit_test --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 5 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --diffdock_pocket --tfn_use_aa_identities --self_condition_x

HarmonicFlow on Binding MOAD with residue identities:

CUDA_VISIBLE_DEVICES="0" python -m train --run_name HarmonicFlow_moad_RadPock --wandb --batch_size 4 --train_split_path index/moad_mmseqs_30sim_train --val_split_path index/moad_mmseqs_30sim_val --predict_split_path index/moad_mmseqs_30sim_test --data_dir data/moad/moad_prepared --data_source moad --biounit1_only --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --num_all_res_train_epochs 100000 --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --diffdock_pocket --tfn_use_aa_identities --self_condition_x --correct_moad_lig_selection --flow_matching --flow_matching_sigma 0.5

CUDA_VISIBLE_DEVICES="0" python -m train --run_name EigenFold_moad_RadPock --wandb --batch_size 4 --train_split_path index/moad_mmseqs_30sim_train --val_split_path index/moad_mmseqs_30sim_val --predict_split_path index/moad_mmseqs_30sim_test --data_dir data/moad/moad_prepared --data_source moad --biounit1_only --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --num_all_res_train_epochs 100000 --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --diffdock_pocket --tfn_use_aa_identities --self_condition_x --correct_moad_lig_selection

Binding Site Recovery PDBBind

CUDA_VISIBLE_DEVICES="0" python -m train --run_name noLig_pdbbind --wandb --batch_size 16 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --pocket_residue_cutoff 12 --clamp_loss 10 --batch_norm --gradient_clip_val 1 --save_inference --check_nan_grads --residue_loss_weight 1 --use_true_pos --use_inv --ignore_lig

CUDA_VISIBLE_DEVICES="0" python -m train --run_name 2dLig_pdbbind --wandb --batch_size 16 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --pocket_residue_cutoff 12 --clamp_loss 10 --batch_norm --gradient_clip_val 1 --save_inference --check_nan_grads --residue_loss_weight 1 --use_true_pos --use_inv --ignore_lig --lig2d_mpnn --lig_mpnn_layers 4 --lig2d_batch_norm

CUDA_VISIBLE_DEVICES="0" python -m train --run_name randomLigPos_pdbbind --wandb --batch_size 16 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --pocket_residue_cutoff 12 --clamp_loss 10 --layer_norm --gradient_clip_val 1 --save_inference --check_nan_grads --residue_loss_weight 1 --use_true_pos --use_inv --mask_lig_translation --mask_lig_pos

CUDA_VISIBLE_DEVICES="0" python -m train --run_name groundTruthPospdbbind --wandb --batch_size 16 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --pocket_residue_cutoff 12 --clamp_loss 10 --batch_norm --gradient_clip_val 1 --save_inference --check_nan_grads --residue_loss_weight 1 --use_true_pos --use_inv --fake_constant_dur 10000 --fake_decay_dur 40 --fake_ratio_start 0.2 --fake_ratio_end 0.2

Binding Site Recovery MOAD:

CUDA_VISIBLE_DEVICES="0" python -m train --run_name noLig_moad --wandb --batch_size 16 --train_split_path index/moad_mmseqs_30sim_train --val_split_path index/moad_mmseqs_30sim_val --predict_split_path index/moad_mmseqs_30sim_test --data_dir data/moad/moad_prepared --data_source moad --biounit1_only --pocket_residue_cutoff 12 --clamp_loss 10 --batch_norm --gradient_clip_val 1 --save_inference --check_nan_grads --residue_loss_weight 1 --use_true_pos --use_inv --min_num_contacts 2 --correct_moad_lig_selection --ignore_lig

CUDA_VISIBLE_DEVICES="0" python -m train --run_name 2dLig_moad --wandb --batch_size 16 --train_split_path index/moad_mmseqs_30sim_train --val_split_path index/moad_mmseqs_30sim_val --predict_split_path index/moad_mmseqs_30sim_test --data_dir data/moad/moad_prepared --data_source moad --biounit1_only --pocket_residue_cutoff 12 --clamp_loss 10 --batch_norm --gradient_clip_val 1 --save_inference --check_nan_grads --residue_loss_weight 1 --use_true_pos --use_inv --ignore_lig --lig2d_mpnn --lig_mpnn_layers 4 --lig2d_batch_norm --min_num_contacts 2 --correct_moad_lig_selection

CUDA_VISIBLE_DEVICES="0" python -m train --run_name randomLigPos_moad --wandb --batch_size 16 --train_split_path index/moad_mmseqs_30sim_train --val_split_path index/moad_mmseqs_30sim_val --predict_split_path index/moad_mmseqs_30sim_test --data_dir data/moad/moad_prepared --data_source moad --biounit1_only --pocket_residue_cutoff 12 --clamp_loss 10 --layer_norm --gradient_clip_val 1 --save_inference --check_nan_grads --residue_loss_weight 1 --use_true_pos --use_inv --mask_lig_translation --mask_lig_pos --correct_moad_lig_selection --min_num_contacts 2

CUDA_VISIBLE_DEVICES="0" python -m train --run_name groundTruthPosmoad --wandb --batch_size 16 --train_split_path index/moad_mmseqs_30sim_train --val_split_path index/moad_mmseqs_30sim_val --predict_split_path index/moad_mmseqs_30sim_test --data_dir data/moad/moad_prepared --data_source moad --biounit1_only --pocket_residue_cutoff 12 --clamp_loss 10 --batch_norm --gradient_clip_val 1 --save_inference --check_nan_grads --residue_loss_weight 1 --use_true_pos --use_inv --min_num_contacts 2 --correct_moad_lig_selection --fake_constant_dur 100000 --fake_decay_dur 10 --fake_ratio_start 0.2 --fake_ratio_end 0.2

Flow matching investigation / ablation on PDBBind:

CUDA_VISIBLE_DEVICES="0" python -m train --run_name StandardTFNLayers --wandb --batch_size 4 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 4 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --diffdock_pocket --tfn_use_aa_identities --self_condition_x --fixed_lig_pos --update_last_when_fixed 

CUDA_VISIBLE_DEVICES="0" python -m train --run_name EigenFold_diffusion --wandb --batch_size 4 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --diffdock_pocket --tfn_use_aa_identities

CUDA_VISIBLE_DEVICES="0" python -m train --run_name VelocityPrediction --wandb --batch_size 4 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 4 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --diffdock_pocket --tfn_use_aa_identities --self_condition_x --velocity_prediction

CUDA_VISIBLE_DEVICES="0" python -m train --run_name NoSelfConditioning --wandb --batch_size 4 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --diffdock_pocket --tfn_use_aa_identities

CUDA_VISIBLE_DEVICES="0" python -m train --run_name HarmonicFlow_sigma0 --wandb --batch_size 4 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --diffdock_pocket --tfn_use_aa_identities --self_condition_x

CUDA_VISIBLE_DEVICES="0" python -m train --run_name HarmonicFlow_sigma0.5 --wandb --batch_size 4 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --clamp_loss 10 --epochs 150 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --pocket_residue_cutoff 12 --ns 32 --nv 8 --diffdock_pocket --tfn_use_aa_identities --self_condition_x

HarmonicFlow blind docking on PDBBind:

CUDA_VISIBLE_DEVICES="0" python -m train --run_name HarmonicFlow_blind_seqSimSplit --wandb --batch_size 4 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --clamp_loss 10 --epochs 250 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 5 --cross_radius 50 --protein_radius 30 --lig_radius 50 --full_prot_diffusion_center --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x 

CUDA_VISIBLE_DEVICES="0" python -m train --run_name HarmonicFlow_blind_timesplit --wandb --batch_size 4 --train_split_path index/timesplit_no_lig_overlap_train --val_split_path index/timesplit_no_lig_overlap_val --predict_split_path index/timesplit_test --clamp_loss 10 --epochs 250 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 5 --cross_radius 50 --protein_radius 30 --lig_radius 50 --full_prot_diffusion_center --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x 

CUDA_VISIBLE_DEVICES="0" python -m train --run_name HarmonicFlow_blind_seqSimSplit_noResidueIdentities --wandb --batch_size 4 --train_split_path index/pdbbind_mmseqs_30sim_train.txt --val_split_path index/pdbbind_mmseqs_30sim_val.txt --predict_split_path index/pdbbind_mmseqs_30sim_test.txt --clamp_loss 10 --epochs 250 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 5 --cross_radius 50 --protein_radius 30 --lig_radius 50 --full_prot_diffusion_center --ns 32 --nv 8 

CUDA_VISIBLE_DEVICES="0" python -m train --run_name HarmonicFlow_blind_timesplit_noResidueIdentities --wandb --batch_size 4 --train_split_path index/timesplit_no_lig_overlap_train --val_split_path index/timesplit_no_lig_overlap_val --predict_split_path index/timesplit_test --clamp_loss 10 --epochs 250 --num_inference 10 --gradient_clip_val 1 --save_inference --check_nan_grads --use_tfn --time_condition_tfn --correct_time_condition --time_condition_inv --time_condition_repeat --flow_matching --flow_matching_sigma 0.5 --prior_scale 1 --layer_norm --tfn_detach --max_lig_size 150 --num_workers 4 --check_val_every_n_epoch 5 --cross_radius 50 --protein_radius 30 --lig_radius 50 --full_prot_diffusion_center --ns 32 --nv 8

amorehead / FlowSite