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eval_configs.py
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class Args:
metadata_path = 'data/metadata_eval.csv'
ckpt_path = 'checkpoint/enzymeflow_mini.ckpt'
#1 data arguments
min_t = 0.0
max_t = 1.0
num_t = 100
#2 flow matcher arguments
ot_plan = False
flow_trans = True
flow_rot = True
# amino-acid flow
flow_aa = True
discrete_flow_type = 'masking'
# msa flow
flow_msa = True
# ec flow
flow_ec = True
class r3:
min_b = 0.01
min_sigma = 0.01
max_b = 20.0
coordinate_scaling = 1. #dont scale coordinates
g=0.1
class so3:
min_sigma = 0.01
max_sigma = 1.5
axis_angle = True
inference_scaling = 0.01
g = 0.1
#3 model arguments
guide_by_condition = True
pretrain_kd_pred = True
num_aa_type = 20 #fixed
num_atom_type = 95 #fixed
node_embed_size = 256
edge_embed_size = 128
dropout = 0.
num_rbf_size = 16
ligand_rbf_d_min = 0.05
ligand_rbf_d_max = 6.
bb_ligand_rbf_d_min = 0.5
bb_ligand_rbf_d_max = 6.
#4 MSA arguments
class msa:
num_msa = 1
num_msa_vocab = 64 #fixed
num_msa_token = 500
msa_layers = 2
msa_heads = 4
msa_hidden_size = 128
msa_embed_size = 32 #node_embed_size
class ec:
num_ec_class = 6 #fixed
ec_heads = 4
ec_embed_size = 32 #node_embed_size
class mpnn:
num_edge_type = 4
dropout = 0.
n_timesteps = 16
mpnn_layers = 3
mpnn_node_embed_size = 256 #node_embed_size
mpnn_edge_embed_size = 128 #edge_embed_size
class embed:
c_s = 256 #node_embed_size
c_pos_emb = 128
c_timestep_emb = 128
timestep_int = 1000
c_z = 128 #edge_embed_size
embed_self_conditioning = True
relpos_k = 64
feat_dim = 64
num_bins = 22
class ipa:
c_s = 256 #node_embed_size
c_z = 128 #edge_embed_size
c_hidden = 16
# c_skip = 16
no_heads = 8
no_qk_points = 8
no_v_points = 12
seq_tfmr_num_heads = 4
seq_tfmr_num_layers = 4
num_blocks = 20
coordinate_scaling = .1 #r3.coordinate_scaling
#5 evaluation arguments
class eval:
noise_scale = 1.
dist_loss_filter = 8.
sample_from_multinomial = False
eval_dir = 'generated'
record_extra = False
discrete_purity = True
aa_temp = 10.
msa_temp = 10.
ec_temp = 10.
aa_noise = 20.0
msa_noise = 40.0
ec_noise = 0.0
rot_sample_schedule = 'linear'
trans_sample_schedule = 'linear'
n_sample = 100
self_condition = False