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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)
ST_2019_4_20_205
# 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, interactive_plot
from Model_training_utils import prepare_dataset, extract_clean_dataset
from keras import backend as K
import os
os.environ['CUDA_VISIBLE_DEVICES']='1'
import pandas as pd
from bokeh.plotting import output_notebook, show
output_notebook()
<div class="bk-root">
    <a href="https://bokeh.pydata.org" target="_blank" class="bk-logo bk-logo-small bk-logo-notebook"></a>
    <span id="bc1a959d-50dd-4424-9069-a276349d4a6e">Loading BokehJS ...</span>
</div>
task_name = 'T168'
db_name = './data/MT_data_clean_Feb28.csv'
FP_type = 'Circular_2048'
log_path = './logs/SAR_2019_4_20/'
prev_model = './logs/2019_4_20_205/T168_rep0_50.hdf5'
n_layer = 1
SAR_result_dir = log_path
output_sdf_name = log_path + 'T168_chemical_landscape.sdf'
plot_df = landscape_building(task_name, db_name, log_path, FP_type,
                       prev_model, n_layer, 
                       SAR_result_dir, output_sdf_name)
plot_df.head()
==== preparing dataset ... ====
Extracted dataset shape: (396, 3)
Loading raw samples now.
shard_size: 8192
About to start loading CSV from ./logs/MT_2019_4_16_780//temp.csv
Loading shard 1 of size 8192.
Featurizing sample 0
TIMING: featurizing shard 0 took 2.351 s
TIMING: dataset construction took 2.395 s
Loading dataset from disk.
==== calculating transfer values ... ====
WARNING:tensorflow:From /root/anaconda3/envs/deepchem/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:1108: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
==== rendering SAR for chemicals on the landscape ... ====
==== packing sdf file ... ====


/root/anaconda3/envs/deepchem/lib/python3.5/site-packages/rdkit/Chem/PandasTools.py:410: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  if np.issubdtype(type(cell_value), float):
show(interactive_plot(plot_df, x_column = 'coord1', y_column = 'coord2', color_column = task_name, 
                      id_field = 'molregno', value_field = task_name, label_field = 'Label'))
# pick clusters of interest and pack them as an sdf for pharmacophore modeling
from Model_landscape_utils import df2sdf
output_sdf_name = log_path + 'T168_Cluster12.sdf'
smiles_field = 'salt_removed_smi'
id_field = 'molregno'
custom_df = plot_df.loc[plot_df['Label'] == 12]
df2sdf(custom_df, output_sdf_name, smiles_field, id_field)
/root/anaconda3/envs/deepchem/lib/python3.5/site-packages/rdkit/Chem/PandasTools.py:296: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  frame[molCol] = frame[smilesCol].map(Chem.MolFromSmiles)
/root/anaconda3/envs/deepchem/lib/python3.5/site-packages/rdkit/Chem/PandasTools.py:410: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  if np.issubdtype(type(cell_value), float):
# 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, interactive_plot
import os
os.environ['CUDA_VISIBLE_DEVICES']='1'
import pandas as pd
from bokeh.plotting import output_notebook, show
output_notebook()
Using TensorFlow backend.
/root/anaconda3/envs/deepchem/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters




<div class="bk-root">
    <a href="https://bokeh.pydata.org" target="_blank" class="bk-logo bk-logo-small bk-logo-notebook"></a>
    <span id="25765e3b-c1e2-46b9-a915-d517997f562b">Loading BokehJS ...</span>
</div>
# set custom file
custom_file = './data/custom_df.csv'
custom_smi_field = "smiles"
custom_id_field = 'molname'
custom_task_field = 'dummy'

# set the landscape to compare to
landscape_sdf = './logs/SAR_2019_4_20/T168_chemical_landscape.sdf'
task_name = 'T168'
db_name = './data/MT_data_clean_Feb28.csv'
FP_type = 'Circular_2048'
log_path = './logs/'
prev_model = './logs/ST_2019_4_20_205/T168_rep0_50.hdf5'
n_layer = 1
custom_SAR_result_dir = log_path
custom_sdf_name = log_path + 'custom_chemicals_on_T168_landscape.sdf'
plot_df = landscape_positioning(custom_file, custom_smi_field, custom_id_field, custom_task_field,
                        landscape_sdf, task_name, db_name, FP_type, log_path,
                        prev_model, n_layer, custom_SAR_result_dir, custom_sdf_name)
==== preparing dataset ... ====
Extracted dataset shape: (396, 3)
Loading raw samples now.
shard_size: 8192
About to start loading CSV from ./logs/MT_2019_4_16_780//temp.csv
Loading shard 1 of size 8192.
Featurizing sample 0
TIMING: featurizing shard 0 took 2.267 s
TIMING: dataset construction took 2.303 s
Loading dataset from disk.
Extracted dataset shape: (2, 3)
Loading raw samples now.
shard_size: 8192
About to start loading CSV from ./Result/custom_df.csv
Loading shard 1 of size 8192.
Featurizing sample 0
TIMING: featurizing shard 0 took 0.013 s
TIMING: dataset construction took 0.022 s
Loading dataset from disk.
==== calculating transfer values ... ====
WARNING:tensorflow:From /root/anaconda3/envs/deepchem/lib/python3.5/site-packages/keras/backend/tensorflow_backend.py:1108: calling reduce_mean (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.
Instructions for updating:
keep_dims is deprecated, use keepdims instead
==== rendering SAR for chemicals on the landscape ... ====
==== packing sdf file ... ====


/root/anaconda3/envs/deepchem/lib/python3.5/site-packages/rdkit/Chem/PandasTools.py:410: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  if np.issubdtype(type(cell_value), float):
plot_df.head()
show(interactive_plot(plot_df, 'coord1', 'coord2', task_name, 'molregno', task_name, 'Label'))
# pick clusters of interest and pack them as an sdf fur pharmacophore modeling
custom_filter = landscape_df['Label'] == 7
df2sdf(df, output_sdf_name, smiles_field, id_field, custom_filter = None)

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