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.
Feel free to reach out with any questions! [email protected]
FlowSite generative process:
HarmonicFlow multi-ligand structure generation gif:
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, 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
.
-
Download the weights for the trained models from here and place them into the
pocket_gen
directory. -
Here are two example commands for running FlowSite using either of the two models we provide. In the first one the inputs are specified via
data/inference_csv_example.csv
and in the second via commandline arguments. The outputs are saved toout_dir
.
CUDA_VISIBLE_DEVICES="0" python -m inference --num_inference 10 --out_dir data/inference_out --csv_file data/inference_csv_example.csv --batch_size 16 --checkpoint pocket_gen/lf5t55w4/checkpoints/best.ckpt --run_test --run_name inference1 --wandb --layer_norm --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 48 --nv 12 --batch_norm --self_condition_x --self_condition_inv --no_tfn_self_condition_inv --self_fancy_init --pocket_residue_cutoff 12
CUDA_VISIBLE_DEVICES="0" python -m inference --num_inference 10 --out_dir data/inference_out2 --design_residues "A60-65,A232,A233,A212-215,A325" --ligand data/2fc2_HEM_HBI_HAR_NO.mol2 --protein data/2fc2_unit1_protein.pdb --batch_size 16 --pocket_def_ligand data/2fc2_HEM_HBI_HAR_NO.mol2 --checkpoint pocket_gen/lf5t55w4/checkpoints/best.ckpt --run_test --run_name inference1 --wandb --layer_norm --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 48 --nv 12 --batch_norm --self_condition_x --self_condition_inv --no_tfn_self_condition_inv --self_fancy_init --pocket_residue_cutoff 12
- You can specify the ligand either via a file using
--ligand
or as a smiles using--smiles
. - The important command line arguments are the following:
--out_dir type=str, default='data/inference_out' : Path to output directory.
--num_inference type=int, default=1 : How many sequences to generate for each complex.
--csv_file type=str, default='data/inference_csv_example.csv' : Path to a CSV file where you can specify the inputs below for multiple complexes.
--ligand type=str, default=None : Path to a ligand file. Either this or the smiles must be specified (or the csv).
--smiles type=str, default=None : Ligand as smiles string. Either this or the ligand as path must be specified (or the csv).
--protein type=str, default=None : Path to a protein file
--design_residues type=str, default=None : Residues you want to design. In this format of chain ID and residue number(s): "A60-65,A232,A233,B212-215,B325"
--pocket_def_ligand type=str, default=None : Path to a ligand file via which the pocket should be specified in the Distance pocket definition. E.g., an .sdf, .mol2 or .pdb file.
--pocket_def_residues type=str, default=None : Residues that you think would be close to the bound ligand for specifying the pocket in a format like this: "A60-65,A232,A233,A212-215,A325"
--pocket_def_center type=str, default=None : String to define a pocket center like this: "-0.214,30.197,9.017"
- Using
pocket_def_ligand
will use all residues close to that ligand as the pocket. - If you use
pocket_def_residues
orpocket_def_center
to define your pocket, we recommend using a different set of model weights:--checkpoint pocket_gen/b1ribx1a/checkpoints/best.ckpt
and--ns 32 --nv 8
instead of--ns 48 --nv 12
.
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 pathdata/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 pathdata/moad/BindingMOAD_2020
- run
python moad_unit2pdb.py
Run on the test set using trained model weights like this (model weights are here):
CUDA_VISIBLE_DEVICES="2" python -m train --run_name test_HarmonicFlow_timesplit_DistPock --wandb --run_test --checkpoint pocket_gen/duw71q7p/checkpoints/best.ckpt --lr 1e-3 --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 --num_all_res_train_epochs 100000 --fake_constant_dur 0 --fake_decay_dur 0 --fake_ratio_start 0 --fake_ratio_end 0 --residue_loss_weight 0 --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 200 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_type ca_distance --pocket_residue_cutoff 14
CUDA_VISIBLE_DEVICES="0" python -m train --run_name test_FlowSite_pdbbind_seqSimSplit --wandb --run_test --checkpoint pocket_gen/89b8ojq8/checkpoints/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 10 --layer_norm --gradient_clip_val 1 --save_inference --check_nan_grads --num_all_res_train_epochs 100000 --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 --self_fancy_init
If training stopped for some reason you can restart by using --checkpoint wandbProjectName/<WANDB run id goes here>/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_SeqSimSplit_DistPock --wandb --lr 1e-3 --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 --check_nan_grads --num_all_res_train_epochs 100000 --fake_constant_dur 0 --fake_decay_dur 0 --fake_ratio_start 0 --fake_ratio_end 0 --residue_loss_weight 0 --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 200 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_type ca_distance --pocket_residue_cutoff 14
CUDA_VISIBLE_DEVICES="2" python -m train --run_name HarmonicFlow_timesplit_DistPock --wandb --lr 1e-3 --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 --num_all_res_train_epochs 100000 --fake_constant_dur 0 --fake_decay_dur 0 --fake_ratio_start 0 --fake_ratio_end 0 --residue_loss_weight 0 --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 200 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_type ca_distance --pocket_residue_cutoff 14
CUDA_VISIBLE_DEVICES="5" python -m train --run_name HarmonicFlow_timesplit_RadPock --wandb --lr 1e-3 --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 --num_all_res_train_epochs 100000 --fake_constant_dur 0 --fake_decay_dur 0 --fake_ratio_start 0 --fake_ratio_end 0 --residue_loss_weight 0 --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 200 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_type radius --radius_pocket_buffer 7
CUDA_VISIBLE_DEVICES="7" python -m train --run_name HarmonicFlow_SeqSimSplit_RadPock --wandb --lr 1e-3 --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 --check_nan_grads --num_all_res_train_epochs 100000 --fake_constant_dur 0 --fake_decay_dur 0 --fake_ratio_start 0 --fake_ratio_end 0 --residue_loss_weight 0 --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 200 --num_workers 4 --check_val_every_n_epoch 1 --cross_radius 50 --protein_radius 30 --lig_radius 50 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_type radius --radius_pocket_buffer 7
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 14 --pocket_type distance --ns 32 --nv 8 --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 14 --pocket_type distance --ns 32 --nv 8 --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 --pocket_type distance --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 --pocket_type distance --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 --pocket_type distance --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 --pocket_type distance --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 --pocket_type distance --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 --pocket_type distance --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 --pocket_type distance --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 --pocket_type distance --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 gaussian_prior --wandb --lr 1e-3 --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 --check_nan_grads --num_all_res_train_epochs 100000 --fake_constant_dur 0 --fake_decay_dur 0 --fake_ratio_start 0 --fake_ratio_end 0 --residue_loss_weight 0 --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 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x --gaussian_prior --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_type radius --radius_pocket_buffer 7 --checkpoint pocket_gen/rq5qv269/checkpoints/best.ckpt --run_test --project pocket_gen_inf
CUDA_VISIBLE_DEVICES="1" python -m train --run_name no_self-conditioning --wandb --lr 1e-3 --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 --check_nan_grads --num_all_res_train_epochs 100000 --fake_constant_dur 0 --fake_decay_dur 0 --fake_ratio_start 0 --fake_ratio_end 0 --residue_loss_weight 0 --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 --ns 32 --nv 8 --tfn_use_aa_identities --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_type radius --radius_pocket_buffer 7 --checkpoint pocket_gen/lxyz3mgy/checkpoints/best.ckpt --run_test --project pocket_gen_inf
CUDA_VISIBLE_DEVICES="4" python -m train --run_name velocity_prediction --wandb --lr 1e-3 --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 --check_nan_grads --num_all_res_train_epochs 100000 --fake_constant_dur 0 --fake_decay_dur 0 --fake_ratio_start 0 --fake_ratio_end 0 --residue_loss_weight 0 --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 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x --velocity_prediction --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_type radius --radius_pocket_buffer 7 --checkpoint pocket_gen/wxlghchs/checkpoints/best.ckpt --run_test --project pocket_gen_inf
CUDA_VISIBLE_DEVICES="6" python -m train --run_name standard_tfn_layers --wandb --lr 1e-3 --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 --check_nan_grads --num_all_res_train_epochs 100000 --fake_constant_dur 0 --fake_decay_dur 0 --fake_ratio_start 0 --fake_ratio_end 0 --residue_loss_weight 0 --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 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x --fixed_lig_pos --update_last_when_fixed --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_type radius --radius_pocket_buffer 7 --checkpoint pocket_gen/7fe03ok3/checkpoints/best.ckpt --run_test --project pocket_gen_inf
CUDA_VISIBLE_DEVICES="2" python -m train --run_name HarmonicFlow_sig0 --wandb --lr 1e-3 --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 --check_nan_grads --num_all_res_train_epochs 100000 --fake_constant_dur 0 --fake_decay_dur 0 --fake_ratio_start 0 --fake_ratio_end 0 --residue_loss_weight 0 --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 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_type radius --radius_pocket_buffer 7 --checkpoint pocket_gen/o8pnsut9/checkpoints/best.ckpt --run_test --project pocket_gen_inf
CUDA_VISIBLE_DEVICES="3" python -m train --run_name HarmonicFlow_sig05 --wandb --lr 1e-3 --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 --check_nan_grads --num_all_res_train_epochs 100000 --fake_constant_dur 0 --fake_decay_dur 0 --fake_ratio_start 0 --fake_ratio_end 0 --residue_loss_weight 0 --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 --ns 32 --nv 8 --tfn_use_aa_identities --self_condition_x --pocket_residue_cutoff_sigma 0.5 --pocket_center_sigma 0.2 --pocket_type radius --radius_pocket_buffer 7 --checkpoint pocket_gen/3nvlg8qg/checkpoints/best.ckpt --run_test --project pocket_gen_inf
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