DrugGEN / app.py
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import streamlit as st
from trainer import Trainer
st.title("Hello World")
class DrugGENConfig:
submodel='CrossLoss'
act='relu'
z_dim=16
max_atom=45
lambda_gp=1
dim=128
depth=1
heads=8
dec_depth=1
dec_heads=8
dec_dim=128
mlp_ratio=3
warm_up_steps=0
dis_select='mlp'
init_type='normal'
batch_size=128
epoch=50
g_lr=0.00001
d_lr=0.00001
g2_lr=0.00001
d2_lr=0.00001
dropout=0.
dec_dropout=0.
n_critic=1
beta1=0.9
beta2=0.999
resume_iters=None
clipping_value=2
features=False
test_iters=10_000
num_test_epoch=30_000
inference_sample_num=1000
num_workers=1
mode="inference"
inference_iterations=100
inf_batch_size=1
protein_data_dir='data/akt'
drug_index='data/drug_smiles.index'
drug_data_dir='data/akt'
mol_data_dir='data'
log_dir='experiments/logs'
model_save_dir='experiments/models'
inference_model=""
sample_dir='experiments/samples'
result_dir="experiments/tboard_output"
dataset_file="chembl45_train.pt"
drug_dataset_file="akt_train.pt"
raw_file='data/chembl_train.smi'
drug_raw_file="data/akt_train.smi"
inf_dataset_file="chembl45_test.pt"
inf_drug_dataset_file='akt_test.pt'
inf_raw_file='data/chembl_test.smi'
inf_drug_raw_file="data/akt_test.smi"
log_sample_step=1000
with st.spinner('Setting up the trainer class...'):
trainer = Trainer(DrugGENConfig())
with st.spinner('Generating Molecules...'):
trainer.inference()