Awesome
<span style="color:#016fff;">FlowSite</span> and <span style="color:#f99e01;">HarmonicFlow</span>
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.
Feel free to reach out with 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, 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
-
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
.
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 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
Running on the test set with trained models
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
Retrain HarmonicFlow and FlowSite
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