This project aims to train neural networks by compound-protein interactions and provides interpretation of the learned model by interactively showing transformed chemical landscape and visualized SAR for chemicals of interest.
Project description
Visar Tutorial
This project aims to train neural networks by compound-protein interactions and provides interpretation of the learned model by interactively showing transformed chemical landscape and visualized SAR for chemicals of interest.
model training
import os
from Model_training_utils import ST_model_hyperparam_screen, ST_model_training
os.environ['CUDA_VISIBLE_DEVICES']='1'
# initialize parameters
task_names = ['T107', 'T108','T51',
'T106','T105', 'T10618','T227', 'T168', 'T10624', 'T10627', 'T10209']
MT_dat_name = './data/MT_data_clean_Feb28.csv'
FP_type = 'Circular_2048'
params_dict = {
"n_tasks": [1],
"n_features": [2048], ## need modification given FP types
"activation": ['relu'],
"momentum": [.9],
"batch_size": [128],
"init": ['glorot_uniform'],
"learning_rate": [0.01],
"decay": [1e-6],
"nb_epoch": [30],
"dropouts": [.2, .4],
"nb_layers": [1],
"batchnorm": [False],
#"layer_sizes": [(100, 20), (64, 24)],
"layer_sizes": [(1024, 512),(1024,128) ,(512, 128),(512,64),(128,64),(64,32),
(1024,512,128), (512,128,64), (128,64,32)],
"penalty": [0.1]
}
# initialize model setup
import random
import time
random_seed = random.randint(0,1000)
local_time = time.localtime(time.time())
log_path = './logs/'
RUN_KEY = 'ST_%d_%d_%d_%d' % (local_time.tm_year, local_time.tm_mon,
local_time.tm_mday, random_seed)
os.system('mkdir %s%s' % (log_path, RUN_KEY))
print(RUN_KEY)
# hyperparam screening using deepchem
log_output = ST_model_hyperparam_screen(MT_dat_name, task_names, FP_type, params_dict,
log_path = './logs/'+RUN_KEY)
# manually pick the training parameters
best_hyperparams = {'T107': [(512,64,1), 0.4],
'T108': [(512,128,1), 0.2],
'T10209': [(512,64,1), 0.4],
'T105': [(512,128,1), 0.2],
'T106': [(512,64,1), 0.2],
'T10618': [(512,128,1), 0.4],
'T10624': [(512,128,1), 0.2],
'T10627': [(512,64,1), 0.2],
'T168': [(512,128,1), 0.2],
'T227': [(512, 64, 1), 0.4],
'T51': [(512, 128, 64,1), 0.2]
}
# model training
output_df = ST_model_training(MT_dat_name, FP_type,
best_hyperparams, result_path = './logs/'+RUN_KEY)
build landscape and display interactive plot
from Model_landscape_utils import landscape_building
from Model_training_utils import prepare_dataset, extract_clean_dataset
import os
os.environ['CUDA_VISIBLE_DEVICES']='1'
import pandas as pd
from bokeh.plotting import output_notebook, show
output_notebook()
task_name = 'T107'
db_name = './data/MT_data_clean_Feb28.csv'
FP_type = 'Circular_2048'
log_path = './logs/MT_2019_4_16_780/'
prev_model = './logs/ST_2019_3_6_697/T107_rep0_50.hdf5'
n_layer = 1
SAR_result_dir = log_path
output_sdf_name = log_path + 'T107_chemical_landscape.sdf'
landscape_building(task_name, db_name, log_path, FP_type,
prev_model, n_layer,
SAR_result_dir, output_sdf_name)
# pick clusters of interest and pack them as an sdf for pharmacophore modeling
from Model_landscape_utils import sdf2df
landscape_sdf_file = './Result/T107_baseline_landscape.sdf'
landscape_df = sdf2df(landscape_sdf_file)
custom_filter = landscape_df['Label'] == 7
df2sdf(df, output_sdf_name, smiles_field, id_field, custom_filter = None)
# pharmacophore building
home_dir = './Result/'
os.chdir(home_dir)
# prepare ligand conformations
from rdkit import Chem
from rdkit.Chem import AllChem
raw_sdf_file = 'Label_7.sdf'
sdf_file = home_dir + 'Label7_rdkit_conf.sdf'
ms = [x for x in Chem.SDMolSupplier(raw_sdf_file)]
n_conf = 5
w = Chem.SDWriter(sdf_file)
for i in range(n_conf):
ms_addH = [Chem.AddHs(m) for m in ms]
for m in ms_addH:
AllChem.EmbedMolecule(m)
AllChem.MMFFOptimizeMoleculeConfs(m)
w.write(m)
# process pharmacophores
result_dir = home_dir + 'Label7_rdkit_phars/'
output_name = 'Cluster7_'
proceed_pharmacophore(home_dir, sdf_file, result_dir, output_name)
# visualize the pharmacophore model in pymol
analysis of custom chemicals
from Model_landscape_utils import landscape_positioning
import os
os.environ['CUDA_VISIBLE_DEVICES']='1'
import pandas as pd
from bokeh.plotting import output_notebook
output_notebook()
# set custom file
custom_file = './Result/custom_df.csv'
custom_smi_field = "smiles"
custom_id_field = 'molname'
custom_task_field = 'dummy'
# set the landscape to compare to
task_name = 'T107'
db_name = './data/MT_data_clean_Feb28.csv'
FP_type = 'Circular_2048'
log_path = './logs/MT_2019_4_16_780/'
prev_model = './logs/ST_2019_3_6_697/T107_rep0_50.hdf5'
n_layer = 1
custom_SAR_result_dir = log_path
custom_sdf_name = log_path + 'custom_chemicals_on_T107_landscape.sdf'
landscape_positioning(custom_file, custom_smi_field, custom_id_field, custom_task_field,
task_name, db_name, FP_type, log_path,
prev_model, n_layer, custom_SAR_result_dir, custom_sdf_name)
# pick clusters of interest and pack them as an sdf fur pharmacophore modeling
from Model_landscape_utils import sdf2df
landscape_sdf_file = './Result/T107_baseline_landscape.sdf'
landscape_df = sdf2df(landscape_sdf_file)
custom_filter = landscape_df['Label'] == 7
df2sdf(df, output_sdf_name, smiles_field, id_field, custom_filter = None)
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