#!/usr/bin/env python
from __future__ import print_function, division
import numpy as np
from rdkit import Chem
from rdkit import rdBase
from rdkit.Chem import AllChem
from rdkit import DataStructs
from sklearn import svm
import time
import pickle
import re
import threading
import pexpect
rdBase.DisableLog('rdApp.error')
"""Scoring function should be a class where some tasks that are shared for every call
can be reallocated to the __init__, and has a __call__ method which takes a single SMILES of
argument and returns a float. A multiprocessing class will then spawn workers and divide the
list of SMILES given between them.
Passing *args and **kwargs through a subprocess call is slightly tricky because we need to know
their types - everything will be a string once we have passed it. Therefor, we instead use class
attributes which we can modify in place before any subprocess is created. Any **kwarg left over in
the call to get_scoring_function will be checked against a list of (allowed) kwargs for the class
and if a match is found the value of the item will be the new value for the class.
If num_processes == 0, the scoring function will be run in the main process. Depending on how
demanding the scoring function is and how well the OS handles the multiprocessing, this might
be faster than multiprocessing in some cases."""
class no_sulphur():
"""Scores structures based on not containing sulphur."""
kwargs = []
def __init__(self):
pass
def __call__(self, smile):
mol = Chem.MolFromSmiles(smile)
if mol:
has_sulphur = [16 not in [atom.GetAtomicNum() for atom in mol.GetAtoms()]]
return float(has_sulphur)
return 0.0
class tanimoto():
"""Scores structures based on Tanimoto similarity to a query structure.
Scores are only scaled up to k=(0,1), after which no more reward is given."""
kwargs = ["k", "query_structure"]
k = 0.7
query_structure = "Cc1ccc(cc1)c2cc(nn2c3ccc(cc3)S(=O)(=O)N)C(F)(F)F"
def __init__(self):
query_mol = Chem.MolFromSmiles(self.query_structure)
self.query_fp = AllChem.GetMorganFingerprint(query_mol, 2, useCounts=True, useFeatures=True)
def __call__(self, smile):
mol = Chem.MolFromSmiles(smile)
if mol:
fp = AllChem.GetMorganFingerprint(mol, 2, useCounts=True, useFeatures=True)
score = DataStructs.TanimotoSimilarity(self.query_fp, fp)
score = min(score, self.k) / self.k
return float(score)
return 0.0
class activity_model():
"""Scores based on an ECFP classifier for activity."""
kwargs = ["clf_path"]
clf_path = 'data/clf.pkl'
def __init__(self):
with open(self.clf_path, "rb") as f:
self.clf = pickle.load(f)
def __call__(self, smile):
mol = Chem.MolFromSmiles(smile)
if mol:
fp = activity_model.fingerprints_from_mol(mol)
score = self.clf.predict_proba(fp)[:, 1]
return float(score)
return 0.0
@classmethod
def fingerprints_from_mol(cls, mol):
fp = AllChem.GetMorganFingerprint(mol, 3, useCounts=True, useFeatures=True)
size = 2048
nfp = np.zeros((1, size), np.int32)
for idx,v in fp.GetNonzeroElements().items():
nidx = idx%size
nfp[0, nidx] += int(v)
return nfp
class Worker():
"""A worker class for the Multiprocessing functionality. Spawns a subprocess
that is listening for input SMILES and inserts the score into the given
index in the given list."""
def __init__(self, scoring_function=None):
"""The score_re is a regular expression that extracts the score from the
stdout of the subprocess. This means only scoring functions with range
0.0-1.0 will work, for other ranges this re has to be modified."""
self.proc = pexpect.spawn('./multiprocess.py ' + scoring_function,
encoding='utf-8')
print(self.is_alive())
def __call__(self, smile, index, result_list):
self.proc.sendline(smile)
output = self.proc.expect([re.escape(smile) + " 1\.0+|[0]\.[0-9]+", 'None', pexpect.TIMEOUT])
if output is 0:
score = float(self.proc.after.lstrip(smile + " "))
elif output in [1, 2]:
score = 0.0
result_list[index] = score
def is_alive(self):
return self.proc.isalive()
class Multiprocessing():
"""Class for handling multiprocessing of scoring functions. OEtoolkits cant be used with
native multiprocessing (cant be pickled), so instead we spawn threads that create
subprocesses."""
def __init__(self, num_processes=None, scoring_function=None):
self.n = num_processes
self.workers = [Worker(scoring_function=scoring_function) for _ in range(num_processes)]
def alive_workers(self):
return [i for i, worker in enumerate(self.workers) if worker.is_alive()]
def __call__(self, smiles):
scores = [0 for _ in range(len(smiles))]
smiles_copy = [smile for smile in smiles]
while smiles_copy:
alive_procs = self.alive_workers()
if not alive_procs:
raise RuntimeError("All subprocesses are dead, exiting.")
# As long as we still have SMILES to score
used_threads = []
# Threads name corresponds to the index of the worker, so here
# we are actually checking which workers are busy
for t in threading.enumerate():
# Workers have numbers as names, while the main thread cant
# be converted to an integer
try:
n = int(t.name)
used_threads.append(n)
except ValueError:
continue
free_threads = [i for i in alive_procs if i not in used_threads]
for n in free_threads:
if smiles_copy:
# Send SMILES and what index in the result list the score should be inserted at
smile = smiles_copy.pop()
idx = len(smiles_copy)
t = threading.Thread(target=self.workers[n], name=str(n), args=(smile, idx, scores))
t.start()
time.sleep(0.01)
for t in threading.enumerate():
try:
n = int(t.name)
t.join()
except ValueError:
continue
return np.array(scores, dtype=np.float32)
class Singleprocessing():
"""Adds an option to not spawn new processes for the scoring functions, but rather
run them in the main process."""
def __init__(self, scoring_function=None):
self.scoring_function = scoring_function()
def __call__(self, smiles):
scores = [self.scoring_function(smile) for smile in smiles]
return np.array(scores, dtype=np.float32)
def get_scoring_function(scoring_function, num_processes=None, **kwargs):
"""Function that initializes and returns a scoring function by name"""
scoring_function_classes = [no_sulphur, tanimoto, activity_model]
scoring_functions = [f.__name__ for f in scoring_function_classes]
scoring_function_class = [f for f in scoring_function_classes if f.__name__ == scoring_function][0]
if scoring_function not in scoring_functions:
raise ValueError("Scoring function must be one of {}".format([f for f in scoring_functions]))
for k, v in kwargs.items():
if k in scoring_function_class.kwargs:
setattr(scoring_function_class, k, v)
if num_processes == 0:
return Singleprocessing(scoring_function=scoring_function_class)
return Multiprocessing(scoring_function=scoring_function, num_processes=num_processes)
