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\DeclareUnicodeCharacter{0301}{*************************************}

or 

{\'\i} --> {\'{i}}

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'''This is demo scripts for running n_gram_graph on delaney.'''

from __future__ import print_function

import argparse
import os

import numpy as np
import json

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CondaHTTPError: HTTP None None for url <https://repo.continuum.io/pkgs/free/linux-64/repodata.json.bz2>
Elapsed: None

An HTTP error occurred when trying to retrieve this URL.
HTTP errors are often intermittent, and a simple retry will get you on your way.
SSLError(SSLError(SSLError("bad handshake: Error([('SSL routines', 'ssl3_get_server_certificate', 'certificate verify failed')],)",),),)


  Could not fetch URL https://pypi.python.org/simple/torch-scatter/: There was a problem confirming the ssl certificate: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:847) - skipping

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index_list=(0 1 2 3 4)
task_list=(mu alpha homo lumo gap r2 zpve cv u0 u298 h298 g298)



for index in "${index_list[@]}"; do
    for task in "${task_list[@]}"; do
        mkdir -p ../output/"$index"/gcnn

        LD_LIBRARY_PATH="${HOME}/my_libc_env/lib/x86_64-linux-gnu/:${HOME}/my_libc_env/usr/lib64/" ${HOME}/my_libc_env/lib/x86_64-linux-gnu/ld-2.17.so `which python` \

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import numpy as np
import deepchem as dc

datasets = ['muv']
models = ['weave']
metrics = [dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean),dc.metrics.Metric(dc.metrics.prc_auc_score, np.mean)]

for model in models:
    print("RUNNING:",model)
    dc.molnet.run_benchmark(datasets,model,test=True,metric=metrics)

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# -*- coding: utf-8 -*-
"""
Created on Mon Mar 06 14:25:40 2017

@author: Zhenqin Wu
"""
from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals

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from __future__ import print_function

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable


class CrossEntropy(nn.Module):
    def __init__(self, alpha=.5):

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from rdkit import RDConfig
from rdkit.Chem import ChemicalFeatures

atom_candidates = ['C', 'Cl', 'I', 'F', 'O', 'N', 'P', 'S', 'Br', 'Unknown']

def one_of_k_encoding(x, allowable_set):
    if x not in allowable_set:
        raise Exception("input {0} not in allowable set{1}:".format(x, allowable_set))
    return map(lambda s: 1 if x == s else 0, allowable_set)

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from sklearn.model_selection import KFold

N = 3867
kf = KFold(n_splits=10, shuffle=True)
for _, test_index in kf.split(range(N)):
    print(len(test_index))

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from rdkit import Chem
from rdkit.Chem import AllChem

raw_smiles = 'CC'
molecule = Chem.MolFromSmiles('CC')
canonical_smiles = Chem.MolToSmiles(molecule)

print raw_smiles
print canonical_smiles