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10f2621 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | # This file is used to explain the meaning of the SurfDock parameters.
# If you want to learn more about the options available for these parameters, please refer ./utils/parsing.py file
# If the user wants to retrain the model, they can pick the right parameters to optimize in their own way
## General arguments
# CUDA optimization parameter for faster training
cudnn_benchmark: true
# use pin_memory or not in linux system
pin_memory: false
# restart dir for training, which storge the model and optimizer
restart_dir: ~/SurfDock/workdir/project_surface_V3_PDBBind_ema_model_pocket_8A
# restart learning rate
restart_lr: null
## dataset
# training data dir
data_dir: ~/PDBBIND/PDBBind_pocket_8A/
# cache path for training data, if you just use SurfDock, you can ignore this
cache_path: ~/PDBBIND/cache_Surface_PDBBIND_pocket_8A
# esm embedding path,if this is set then the LM embeddings at that path will be used for the receptor features
esm_embeddings_path: ~/PDBBIND/esm_embedding/esm_embedding_pocket_for_train/esm2_3billion_embeddings.pt
# dataset split file
split_test: ~/data/splits/timesplit_test
split_train: ~/data/splits/timesplit_no_lig_overlap_train
split_val: ~/data/splits/timesplit_no_lig_overlap_val
# use rmsd matching or not,use default value is fine
matching: true
# Differential evolution maxiter parameter in matching
matching_maxiter: 20
# the number of workers for dataloader
num_dataloader_workers: 1
# the number of complexes to inference in validation set
num_inference_complexes: 500
# the number of workers for training
num_workers: 1
## model
# if you want to use dynamic max cross, you can set this to true. this parameter can set a different max cross distance for each timestep
dynamic_max_cross: true
# scale the noise by sigma or not
scale_by_sigma: true
# Maximum sigma for rotational component
rot_sigma_max: 1.55
# Minimum sigma for rotational component
rot_sigma_min: 0.03
# Maximum sigma for torsional component
tor_sigma_max: 3.14
# Minimum sigma for torsional component
tor_sigma_min: 0.0314
# Maximum sigma for translational component
tr_sigma_max: 5.0
# Minimum sigma for translational component
tr_sigma_min: 0.1
# the weight of torsional component loss
tor_weight: 0.33
# the weight of translational component loss
tr_weight: 0.33
# the weight of rotational component loss
rot_weight: 0.33
# diffusion model type , default value is surface_score_model,when train a scoring model, you can use mdn_model
model_type: surface_score_model
# diffusion model version, use default value is 3
model_version: version3
# training epochs
n_epochs: 2000
# number of gaussians which used to MDN module for socring module
n_gaussians: 20
# use no batch norm or not
no_batch_norm: false
# use torsion or not ,default value is false
no_torsion: false
# the layer number of the model
num_conv_layers: 6
# the dimension of the scalar embedding in e3nn
ns: 48
# the dimension of the vector embedding in e3nn
nv: 10
# use the second order representation or not
use_second_order_repr: false
# embedding type
embedding_type: sinusoidal
# max number of neighbors for each atom in ligand graph
max_radius: 5.0
batch_size: 12 # batch size
# the radius of the receptor graph
receptor_radius: 15.0
# max number of neighbors for each C-alpha atom(residue graph)
c_alpha_max_neighbors: 24
# Maxximum inter-distance about different node types
cross_max_distance: 80
# cross distance embed dimension
cross_distance_embed_dim: 32
# intra-distance embed dimension
distance_embed_dim: 32
# Size of the embedding of the diffusion time
sigma_embed_dim: 32
# Parameter of the diffusion time embedding
embedding_scale: 1000
# dropout rate for dropout layer in diffusion module
dropout: 0.1
# the dropout rate for scoring module
mdn_dropout: 0.1
# use the ema model or not
use_ema: false
# exponential moving average rate
ema_rate: 0.999
# learning rate ,use default value is fine
lr: 0.001
# the learning rate scheduler
scheduler: plateau
# the weight decay factor, use default value is fine
w_decay: 0.0
# the patience of the learning rate scheduler
scheduler_patience: 50
# earlystop goal use max or min
inference_earlystop_goal: max
# inference earlystop metric
inference_earlystop_metric: valinf_rmsds_lt2
# denoise steps in inference stage
inference_steps: 20
# the early stop goal for training scoring module
mdn_early_stop_patience: 30
# remove hydrogen or not
remove_hs: true
# validation inference frequency,default is 20 epochs
val_inference_freq: 20
# top-N atoms with the smallest distances with surface node for mdn calculate in scoring model
topN: 1
# predict bond type or not in training scoring model stage
bond_type_prediction: true
# atom type prediction or not in training scoring model stage
atom_type_prediction: true
## wandb log
# use wandb to log or not
wandb: true
# wandb dir
wandb_dir: ~/wandb/SurfDock
# the project name in wandb
project: SurfDock_V3_PDBBind_ema_model_pocket_8A
# the run name in wandb
run_name: project_surface_V3_PDBBind_ema_model_pocket_8A
# dir of log files
log_dir: ~/wandb/SurfDock/workdir
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