Skip to main content

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)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for visar, version 0.1.3.2
Filename, size File type Python version Upload date Hashes
Filename, size visar-0.1.3.2.tar.gz (17.7 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page