def shape_data(self, mols):
fps = [
GetMorganFingerprintAsBitVect(m.rdmol, 2, nBits=2048) for m in mols
]
fps_arr = np.array(fps).reshape(-1, 2048)
energies = np.array([m.docking_energy for m in mols]).reshape(-1, 1)
self.data = (fps_arr, energies)
def predict(self, mols):
fps = [
GetMorganFingerprintAsBitVect(m.rdmol, 2, nBits=2048) for m in mols
]
fps_arr = np.array(fps).reshape(-1, 2048)
y = self.model.predict(fps_arr)
for i in range(len(y)):
mols[i].predicted_energy = y[i].item()
return y
def create(self, frame):
"""
Returns
-------
"""
from rdkit.Chem import MolFromSmiles
from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect
smiles = self._get_smiles(frame)
mol = MolFromSmiles(smiles)
fps = GetMorganFingerprintAsBitVect(mol, radius=self.radius, nBits=self.length)
fps = np.array(fps, dtype='float64')
return {'acronym': self.acronym, 'descriptors': fps}
def feature_fingerprint_morgan(molecules, radius, bits):
"""
Generates circular morgan fingerprints for each molecule
:param molecules: list of RDKit molecules
:param radius: radius for morgan fingerprint generation
:param bits: length of bit vector
:return: list of fingerprints
"""
feature_vector = []
for mol in molecules:
# radius 3 equals ecpf6 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4510302/
string_bits = GetMorganFingerprintAsBitVect(mol, radius,
nBits=bits).ToBitString()
feature_vector.append(list(map(int, string_bits)))
return feature_vector
def make_fingerprints(length, verbose, mols, chosen=None):
if chosen == 1:
fp_list = [
fingerprint(lambda x : GetHashedAtomPairFingerprintAsBitVect(x, nBits = length),
"&qfuot;Atom pair (1985)")]
elif chosen == 2:
fp_list = [
fingerprint(lambda x : GetHashedTopologicalTorsionFingerprintAsBitVect(x, nBits = length),
"Topological torsion (1987)")]
elif chosen == 3:
fp_list = [
fingerprint(lambda x : GetMorganFingerprintAsBitVect(x, 3, nBits = length),
"Morgan circular ")]
elif chosen == 4:
fp_list = [
fingerprint(FingerprintMol, "Estate (1995)")]
elif chosen == 5:
fp_list = [
fingerprint(lambda x: GetAvalonFP(x, nBits=length),
"Avalon bit based (2006)")]
elif chosen == 6:
fp_list = [
fingerprint(lambda x: np.append(GetAvalonFP(x, nBits=length), Descriptors.MolWt(x)),
"Avalon+mol. weight")]
elif chosen == 7:
fp_list = [
fingerprint(lambda x: GetErGFingerprint(x), "ErG fingerprint (2006)")]
elif chosen == 8:
fp_list = [
fingerprint(lambda x : RDKFingerprint(x, fpSize=length),
"RDKit fingerprint")]
elif chosen == 9:
fp_list = [
fingerprint(lambda x : FingerprintMols.FingerprintMol(x),
"RDKit fingerprint2")]
else:
fp_list = [fingerprint(lambda x : MACCSkeys.GenMACCSKeys(x), "RDKit MACCSkeys")]
for fp in fp_list:
if (verbose): print("doing", fp.name)
fp.apply_fp(mols)
return fp_list