def compound_identity(
query: Molmap,
target: Optional[Mapping[str, Mol]]) -> Mapping[str, List[str]]:
target_set = set((target if target is not None else query).keys())
match_sets = {q: target_set.copy() for q in query.keys()}
for fp_type in ["morgan", "topological"]:
query_arena = make_fingerprint_arena(query, fingerprint_type=fp_type)
target_arena = (make_fingerprint_arena(
target, fingerprint_type=fp_type) if target is not None else None)
matches = find_similarity_matches(query_arena,
target_arena,
threshold=1)
for q in match_sets.keys():
match_sets[q] &= set(x for x in matches.get(q, {}).keys())
query_weights = {k: MolWt(m) for k, m in query.items()}
target_weights = ({k: MolWt(m)
for k, m in target.items()}
if target is not None else query_weights)
for q, ts in match_sets.items():
weight_matches = set()
for t in ts:
if isclose(query_weights[q], target_weights[t], rel_tol=0.001):
weight_matches.add(t)
match_sets[q] &= weight_matches
return {k: list(v) for k, v in match_sets.items()}
def keep_biggest(cls, mol_in):
"""Strip small fragments from compound.
Returns a new compound where only the "biggest" fragment is conserved
according to (i) the number of non-Hs atoms and if there is tie then
according to (ii) the molecular weight.
:param mol_in: RDKit Mol
:return mol_out: new RDKit Mol having only one connected component
"""
def count_non_hs_atom(mol):
ans = 0
for atm in mol.GetAtoms():
if atm.GetAtomicNum() != 1:
ans += 1
return ans
# Remove "other" molecules
molfrag = GetMolFrags(mol_in, asMols=True, sanitizeFrags=False)
mol_out = mol_in
if len(molfrag) > 1:
accepted_nbr_atm = 0 # flag number of atoms in fragment
accepted_mw = 0 # flag the molecular weight of the biggest fragment
for f in molfrag:
nbr_atm = count_non_hs_atom(f)
if nbr_atm > accepted_nbr_atm or (nbr_atm == accepted_nbr_atm
and
MolWt(f) > accepted_mass):
accepted_nbr_atm = nbr_atm
accepted_mass = MolWt(f)
mol_out = f # keep only the biggest fragment
cls._copy_properties(mol_in, mol_out) # save the name and stuff
return mol_out
def calculate_mass_route():
"""Calculate compound molecular mass.
---
post:
summary: Calculate compound molecular mass.
requestBody:
required: true
content:
application/json:
schema: CalculateMassSchema
responses:
'200':
content:
application/json:
schema: CalculateMassResultSchema
"""
data = CalculateMassSchema().load(request.json)
compounds, skipped = convert_compound_request(data["compounds"])
mass_out = {}
for n, m in compounds.items():
try:
mass_out[n] = MolWt(m)
except Exception as e:
skipped.append(n)
out = {"mass": mass_out, "skipped": skipped}
CalculateMassResultSchema().validate(out)
return out
def evaluate(self, lst_in):
"""
Evaluate structure alerts on a list of SMILES
:param lst_in: input list of [SMILES, Name]
:return: list of alerts matched or "OK"
"""
smiles, name = lst_in
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return [
smiles, name, 'INVALID', -999, -999, -999, -999, -999, -999
]
desc_list = [
MolWt(mol),
MolLogP(mol),
NumHDonors(mol),
NumHAcceptors(mol),
TPSA(mol),
CalcNumRotatableBonds(mol)
]
for row in self.rule_list:
patt, max_val, desc = row
if len(mol.GetSubstructMatches(patt)) > max_val:
return [smiles, name] + [desc + " > %d" %
(max_val)] + desc_list
return [smiles, name] + ["OK"] + desc_list
def __call__(self, smiles: str):
mol = Chem.MolFromSmiles(smiles)
if not (self.rule_dict["MW"][0] <= MolWt(mol) <=
self.rule_dict["MW"][1]):
return False
if not (self.rule_dict["LogP"][0] <= MolLogP(mol) <=
self.rule_dict["LogP"][1]):
return False
if not (self.rule_dict["HBD"][0] <= NumHDonors(mol) <=
self.rule_dict["HBD"][1]):
return False
if not (self.rule_dict["HBA"][0] <= NumHAcceptors(mol) <=
self.rule_dict["HBA"][1]):
return False
if not (self.rule_dict["TPSA"][0] <= TPSA(mol) <=
self.rule_dict["TPSA"][1]):
return False
for row in self.rule_list:
patt, max_val, desc = row
if len(mol.GetSubstructMatches(patt)) > max_val:
return False
return True
def evaluate(self, point: Any) -> float:
"""
Evaluate a point.
Args:
point: point to evaluate.
Returns:
evaluation for the given point.
"""
latent_point = torch.tensor([[point]])
batch_latent = latent_point.repeat(1, self.batch, 1)
smiles = self.generator.generate_smiles(batch_latent)
mweights = []
for smile in smiles:
try:
mweights.append(MolWt(Chem.MolFromSmiles(smile)))
except Exception:
logger.warning("MW calculation failed.")
if len(mweights) > 0:
return 1.0 - exp(-abs(self.target -
(sum(mweights) / len(mweights))))
else:
return 1.0
def get_similar_molecules(query_descriptors: list, query_smiles: str,
mol_inchi: str, database_binary_path: str,
database_smiles_path: str, database_id: int,
num_to_keep: int):
"""Celery task that reads database binary files comparing query descriptors
Args:
query_descriptors (list): USRCAT descriptors of query.
query_smiles (str): SMILES representation of the query molecule.
database_binary_path (str): Binary file path - must be string, not path, as Path is non-serialisable.
database_smiles_path (str): Smiles file path - same as above, must be a string.
database_id (int): Int representing database ID, used to cache results against specific databases
"""
print("Worker running for " + query_smiles)
mol = Chem.MolFromSmiles(query_smiles)
query_mol_identifier = mol_inchi + "_" + str(database_id) + "_" + str(
num_to_keep)
# CPP program bellow called for speed of processing
###################################################################################
# Build the command line - //Arguments must be
# 0: Executable
# 1: Binary file location without last .bin extension, so that .bin and .smi file locations can be derived
# 2: Number of best to keep
# 3-63: USRCAT descriptors of query
command_line = [
"/home/ubuntu/similarity_lab/utils/usrcat_binary_reader_similarity_lab",
database_binary_path.replace(".bin", ""),
str(num_to_keep)
]
for i in range(60):
command_line.append(str(query_descriptors[i]))
process = Popen(command_line, stdout=PIPE)
output, err = process.communicate()
exit_code = process.wait()
lines = output.decode("utf-8").splitlines()
with open(
Path(app.config['QUERY_SIMILARS_DIRECTORY']) /
(query_mol_identifier + ".csv"), "w") as outfile:
outfile.write(
"Candidate SMILES,USRCAT Score,Morgan Score,eMolecules ID,MW\n")
for line in lines:
stripped_line = line.strip()
candidate_smiles, title_comma_score = stripped_line.split(
" ", maxsplit=1)
candidate_title, candidate_score = title_comma_score.split(",")
candidate_score = float(candidate_score)
candidate_mol = Chem.MolFromSmiles(candidate_smiles)
candidate_morgan_score = DiceSimilarity(
GetMorganFingerprint(candidate_mol, 2),
GetMorganFingerprint(mol, 2))
mw = MolWt(candidate_mol)
outfile.write(
f'{candidate_smiles},{round(candidate_score,3)},{round(candidate_morgan_score,3)},{candidate_title.replace("_1","")},{round(mw,3)}\n'
)
print("Worker done")
def check_lipinski(mol):
fgs = load_functional_groups()
h_donors = Lipinski.NumHDonors(mol.rdmol)
h_acceptors = Lipinski.NumHAcceptors(mol.rdmol)
log_p = MolLogP(mol.rdmol)
wt = MolWt(mol.rdmol)
if h_donors <= 5 and h_acceptors <= 5 and log_p < 5:
if wt >= 450:
mol.join(fgs['terminal_fg'].get_random())
return True, False
else:
return True, False
else:
return False, False
def fragments(self) -> List[Fragment]:
"""Fragments ligand using RDKit.
Includes self as first element.
Returns:
List[Fragment]: Ordered by weight, heaviest first
"""
reactant = self.rdkit_mol
mols = [reactant]
products = Reactor().react(reactant)
mols.extend(products)
mols.sort(key=lambda m: MolWt(m), reverse=True)
return [Fragment(self.atomium_mol, mol) for mol in mols]
def __call__(self, mol):
"""
Returns the QED of a SMILES string or a RdKit molecule.
"""
# Error handling.
if type(mol) == rdkit.Chem.rdchem.Mol:
pass
elif type(mol) == str:
mol = Chem.MolFromSmiles(mol, sanitize=True)
if mol is None:
raise ValueError("Invalid SMILES string.")
else:
raise TypeError("Input must be from {str, rdkit.Chem.rdchem.Mol}")
return MolWt(mol)
def process_by_folder(fd, inpath):
cycle = fd.strip("cycle_")
sd = inpath + '/' + fd + '/ranked_designs.sd'
if os.path.exists(sd):
cir_mols = [PropertyMol(m) for m in Chem.SDMolSupplier(sd)]
for m in cir_mols:
# Calculate properties for each mol
m.SetProp('Cycle', cycle)
m.SetProp('MolWeight', str(MolWt(m)))
m.SetProp('LogP', str(LogP(m)))
m.SetProp('QED', str(QED(m)))
m.SetProp('SAS', str(SAS(m)))
# Select the highest score design in the cycle
# (the first one in the ranked sd file)
best_mol = cir_mols[0]
return cir_mols, best_mol
def drawmol(s):
bsize = 200
tsize = 80
size = (bsize, bsize + tsize)
m = Chem.MolFromSmiles(s)
#print("wt", MolWt(m))
img, canvas, drawer = Draw.MolToImage(m, size=size, returnCanvas=True)
# centerIt=False, drawingTrans=(bsize/2+1,bsize/2+tsize))
font = Font(face='sans', size=12)
pos = bsize / 2, bsize, 0
canvas.addCanvasText('%s\r\nMolWt: %g\tTPSA: %g' % (s, MolWt(m), TPSA(m)),
pos, font)
with open('xx' + s + '.png', 'w') as f:
canvas.flush()
img.save(f)
def smiles_reaction_matrix(smarts, *sources, **kwargs):
sep = kwargs.setdefault('sep', ' ')
molValue = int(kwargs.get('molValue', 400))
logValue = float(kwargs.get('logValue', 4.0))
reaction = ReactionFromSmarts(smarts)
smilesLists = [load_smiles_file(source) for source in sources]
products = reaction_matrix(reaction, *smilesLists)
for reactants, product in products:
cids = [r.GetProp("_Name") for r in reactants]
product_id = '.'.join(cids)
for mol in product:
smiles = MolToSmiles(mol, isomericSmiles=True)
mol.UpdatePropertyCache(strict=False)
mh = AddHs(mol, addCoords=True)
mwt = MolWt(mol)
if mwt <= molValue:
logp = MolLogP(mol)
if logp < logValue:
yield sep.join((smiles, product_id, str(mwt), str(logp)))+"\n"
def cpd_inform(SMILES):
"""
A function for getting compound information from SMILES string it received
a SMILES string and return a dictionary of information consisted of
number of C, H, O , N, P, S, X, Degree of Unsaturation
and Molecular Weight
"""
info = []
mol = Chem.rdmolfiles.MolFromSmiles(SMILES)
info.append(float(count_C(mol)))
info.append(float(count_H(mol)))
info.append(float(count_O(mol)))
info.append(float(count_N(mol)))
info.append(float(count_P(mol)))
info.append(float(count_S(mol)))
info.append(float(count_X(mol)))
info.append((2*info[0] + 2 + info[3] + info[4] - info[6] - info[1])/2) # it is (2*C + 2 + N + P - X - H)/2
info.append(MolWt(mol))
return info
def get_global_features(mol):
"""Computes global-level features for a molecule.
Parameters
----------
mol : rdkit mol
Returns
-------
[np.ndarray]
Global-level features
"""
# MW, TPSA, logP, n.hdonors
mw = MolWt(mol)
tpsa = CalcTPSA(mol)
logp = MolLogP(mol)
n_hdonors = NumHDonors(mol)
desc = np.array([mw, tpsa, logp, n_hdonors], dtype=np.float32)
return desc
def cal_prop(q, return_dict_prop):
nbits = 1024
while True:
qqq = q.get()
if qqq == 'DONE':
# print('proc =', os.getpid())
break
idx, smi = qqq
# if idx%10000==0:
# print(idx)
mol = Chem.MolFromSmiles(smi)
logP = MolLogP(mol)
SAS = sascorer.calculateScore(mol)
QED = qed(mol)
MW = MolWt(mol)
TPSA0 = TPSA(mol)
return_dict_prop[idx] = [logP, SAS, QED, MW, TPSA0]
df["res_stnd_SMILES"] = res_stnd_smi_list # Salt Removed from Standardized SMILES
error_mask2 = np.any(
[df["res_stnd_SMILES"] == "", df["res_stnd_SMILES"] == "-"], axis=0)
df3 = df[np.logical_not(error_mask2)]
print(df3[df3.duplicated("res_SMILES", False)][["Result",
"res_SMILES"]]) # CSV로 하나씩 살피기
df3 = df.drop_duplicates("res_stnd_SMILES") # Drop first duplicated ones
# 5. MW 짜르기
mw_lst = []
for i, row in df3.iterrows():
smi = row["res_stnd_SMILES"]
mol = MolFromSmiles(smi)
mw = MolWt(mol)
mw_lst.append(mw)
df3["MW"] = mw_lst
print(np.mean(df3[df3["MW"] < 40]["Result"] == "P"),
np.mean(df3[df3["MW"] > 800]["Result"] == "P"))
df3_mw = df3[df3["MW"] > 40]
df3_mw = df3_mw[df3_mw["MW"] < 800]
df3_mw = df3_mw.reset_index(drop=True)
seeds = [1043] #np.random.randint(1,1e4,10)
for seed in seeds:
trn, test = split_df(df3_mw, seed) # df3,df3_mw
trn.to_csv(f"{data_path}/TG471_train_all_stdn_curated_mw_{seed}.csv")
test.to_csv(f"{data_path}/TG471_test_all_stdn_curated_mw_{seed}.csv")
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
def str2mol(s):
if isinstance(s, str):
return Chem.MolFromSmiles(s)
else:
return s
rdkit_funcs = {"QED": lambda x: QED(str2mol(x)),
"MOLWT": lambda x: MolWt(str2mol(x)),
"SAS": lambda x: calculateScore(str2mol(x)),
"LOGP": lambda x: LogP(str2mol(x))}
class AttnParams:
_params = None
def __init__(self):
self._params = {
"model": None,
"data": None, # Data stuff
"len_limit": 120,
"num_props": 4,
"current_epoch": 1, # Training params
"epochs": 20,
canvas.addCanvasText('%s\r\nMolWt: %g\tTPSA: %g' % (s, MolWt(m), TPSA(m)),
pos, font)
with open('xx' + s + '.png', 'w') as f:
canvas.flush()
img.save(f)
if __name__ == '__main__':
drawmol('CN1CCC[C@H]1c2cccnc2')
drawmol('CC(=O)OC1=CC=CC=C1C(=O)O')
drawmol('O1C=C[C@H]([C@H]1O2)c3c2cc(OC)c4c3OC(=O)C5=C4CCC(=O)5')
sys.exit(0)
# sample code to use new drawing API (older rdkit do not have DrawString)
from rdkit.Chem.AllChem import EmbedMolecule
assert EmbedMolecule(m) >= 0
x = Draw.rdMolDraw2D.MolDraw2DSVG(200, 250)
x.DrawMolecule(m)
x.DrawString('Test String', 20, 200)
x.FinishDrawing()
print(x.GetDrawingText())
# sample code to generate a legend
legstr = ''
if molname:
legstr += molname + '\n'
legstr += '%s\nWt=%g LogP=%g TPSA=%g\nHBA=%d HBD=%d RotBond=%d\n' % \
(smiles, MolWt(mol), MolLogP(mol), TPSA(mol),
NumHAcceptors(mol), NumHDonors(mol), NumRotatableBonds(mol))
def Girolami(smiles):
# Get RDKit molecule
mol = Chem.MolFromSmiles(smiles)
mol = Chem.AddHs(mol)
# Calculate molecular weight
M = MolWt(mol)
# Iterate over all atoms and get group
# contributions
group_contributions = 0.0
for atom in mol.GetAtoms():
Z = atom.GetAtomicNum()
if Z == 1:
group_contributions += 1
elif 3 <= Z <= 9:
group_contributions += 2
elif 11 <= Z <= 17:
group_contributions += 4
elif 19 <= Z <= 35:
group_contributions += 5
elif 37 <= Z <= 53:
group_contributions += 7.5
elif 55 <= Z <= 83:
group_contributions += 9
else:
raise ValueError('The molecule contains atoms for whom contributions are not defined.')
# Calculate initial density
rho = M / (5 * group_contributions)
# Define functional groups for correction
alcohol = Chem.MolFromSmarts('[OX2H]')
acid = Chem.MolFromSmarts('[CX3](=O)[OX2H1]')
amine = Chem.MolFromSmarts('[NX3;H2,H1;!$(NC=O)]')
sulfoxide = Chem.MolFromSmarts('[$([#16X3]=[OX1]),$([#16X3+][OX1-])]')
sulfone = Chem.MolFromSmarts('[$([#16X4](=[OX1])=[OX1]),$([#16X4+2]([OX1-])[OX1-])]')
n_alcohol = len(mol.GetSubstructMatches(alcohol))
n_acid = len(mol.GetSubstructMatches(acid))
n_amine = len(mol.GetSubstructMatches(amine))
n_sulfoxide = len(mol.GetSubstructMatches(sulfoxide))
n_sulfone = len(mol.GetSubstructMatches(sulfone))
# Find rings
sssr = Chem.GetSymmSSSR(mol)
n_rings = len(sssr)
n_condrings = 0
if n_rings > 1:
for ring in range(n_rings):
for other_ring in range(ring, n_rings):
r1 = sssr[ring]
r2 = sssr[other_ring]
t = 0
for j in r1:
if j in r2:
t += 0
if t >= 2:
n_condrings += 2
n_rings -= n_condrings
# Define groups for corrections
first_group = [n_alcohol, n_acid, n_amine, n_sulfoxide, n_rings]
second_group = [n_sulfone]
third_group = [n_condrings]
# Add corrections
correction = 0.0
for n in first_group:
if correction + n * 0.1 <= 1.3:
correction += n * 0.1
else:
return 1.3 * rho
for n in second_group:
if correction + n * 0.2 <= 1.3:
correction += n * 0.2
else:
return 1.3 * rho
for n in third_group:
if correction + n * 0.075 <= 1.3:
correction += n * 0.075
else:
return 1.3 * rho
return (1 + correction) * rho
def _calculate_phys_chem_property(self, mol):
return MolWt(mol)
def cal_fp_props(mol):
# Wrapper function for multiprocessing
mol_props = Props(MolToSmiles(mol), MolWt(mol), LogP(mol), QED(mol),
SAS(mol))
fp = enc.encode_mol(mol)
return fp, mol_props
def mol_weight_get(smiles):
m = Chem.MolFromSmiles(smiles)
if (m is None):
return ("Could not parse input: " + smiles, 500)
return {"smiles": smiles, "molWeight": MolWt(m)}
def calculate(self):
w = ExactMolWt(self.mol) if self._exact else MolWt(self.mol)
if self._averaged:
w /= self.mol.GetNumAtoms()
return w
def mol_wt(smiles):
"""Get molecular weight (in Daltons)"""
return MolWt(Chem.MolFromSmiles(smiles))
mws = []
logps = []
nhdonors = []
values = []
dataset = []
for data in list(LABEL_GUIDE.keys()) + ["cyp"]:
with open(os.path.join(DATA_PATH, data, f"data_{data}.pt"),
"rb") as handle:
inchis, v = pickle.load(handle)
values.extend(v)
for inchi in tqdm(inchis):
mol = MolFromInchi(inchi)
mws.append(MolWt(mol))
logps.append(MolLogP(mol))
nhdonors.append(NumHDonors(mol))
dataset.append(DATASET_GUIDE[data])
df = pd.DataFrame({
"Molecular weight (gr./mol)": mws,
r"aLog$P$": logps,
"No. hydrogen donors": nhdonors,
"values": values,
"dataset": dataset,
})
f, axs = plt.subplots(1, 3, figsize=(18, 6))
axs[0].grid(alpha=0.5)
df = pd.read_hdf(H5_FILE, h5_table)
idx = np.loadtxt(good_dirs, dtype=np.str)
iidx = [int(i[2:]) for i in idx]
smiles = df.loc[iidx]['smiles']
except:
raise Exception('lock section error')
finally:
lock.release()
n = 0
for i, smile in zip(idx, smiles):
n += 1
# can prepend MolWt with Exact
m = Chem.MolFromSmiles(smile)
m = Chem.AddHs(m)
mw = MolWt(m)
if n < 11:
print('molecule: {} smile: {} mw: {}'.format(i, smile, mw))
mws.append(mw)
print('processed {} molecules!'.format(len(smiles)))
# plot historgrams
from plot_settings import *
def plot_hist(save_path,
data,
xlabel=None,
ylabel=None,
label=None,