visar 0.1.5.1

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.

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()

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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):

	T168	molregno	salt_removed_smi	coord1	coord2	Label	imgs
2597	5.990114	1055209	CCOC(=O)CCCN1CCC(CNC(=O)c2c3n(c4ccccc24)CCCO3)CC1	19.203522	8.912773	7	./logs/MT_2019_4_16_780/1055209_img.png
2598	5.969795	1055210	COC(=O)CCCCCN1CCC(CNC(=O)c2c3n(c4ccccc24)CCCO3)CC1	18.263283	9.447498	7	./logs/MT_2019_4_16_780/1055210_img.png
2599	5.709955	1055211	COC(=O)CCCCCCCCCN1CCC(CNC(=O)c2c3n(c4ccccc24)CCCO3)CC1	18.244911	9.454099	7	./logs/MT_2019_4_16_780/1055211_img.png
2600	5.470047	1055212	COC(=O)CCCCCCCCCCCN1CCC(CNC(=O)c2c3n(c4ccccc24)CCCO3)CC1	18.221170	9.463950	7	./logs/MT_2019_4_16_780/1055212_img.png
2601	5.119977	1055213	CC(C)(C)OC(=O)CN1CCC(CNC(=O)c2c3n(c4ccccc24)CCCO3)CC1	17.685968	7.442045	7	./logs/MT_2019_4_16_780/1055213_img.png

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




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# 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()

	coord1	coord2	molregno	T168	Label	salt_removed_smi	imgs	group
0	14.690001	-11.208886	RIT	0.000000	4	CC1=C(CCN2CCC(=C(c3ccc(F)cc3)c4ccc(F)cc4)CC2)C(=O)N5C=CSC5=N1	./logs/MT_2019_4_16_780/RIT_img.png	1
1	12.472392	-7.515880	ERG	0.000000	11	CN1C[C@@H](C=C2[C@H]1Cc3c[nH]c4cccc2c34)C(=O)N[C@]5(C)O[C@@]6(O)[C@@H]7CCCN7C(=O)[C@H](Cc8ccccc8)N6C5=O	./logs/MT_2019_4_16_780/ERG_img.png	1
2	-12.093712	14.359370	1055209	5.990114	6	CCOC(=O)CCCN1CCC(CNC(=O)c2c3n(c4ccccc24)CCCO3)CC1	./logs/MT_2019_4_16_780/1055209_img.png	0
3	-10.720143	14.633834	1055210	5.969795	6	COC(=O)CCCCCN1CCC(CNC(=O)c2c3n(c4ccccc24)CCCO3)CC1	./logs/MT_2019_4_16_780/1055210_img.png	0
4	-10.718423	14.634096	1055211	5.709955	6	COC(=O)CCCCCCCCCN1CCC(CNC(=O)c2c3n(c4ccccc24)CCCO3)CC1	./logs/MT_2019_4_16_780/1055211_img.png	0

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)