def hard_osimertinib() -> GoalDirectedBenchmark:
smiles = 'COc1cc(N(C)CCN(C)C)c(NC(=O)C=C)cc1Nc2nccc(n2)c3cn(C)c4ccccc34'
modifier = ClippedScoreModifier(upper_x=0.8)
similar_to_osimertinib = TanimotoScoringFunction(smiles,
fp_type='FCFP4',
score_modifier=modifier)
but_not_too_similar = TanimotoScoringFunction(
smiles,
fp_type='ECFP6',
score_modifier=MinGaussianModifier(mu=0.85, sigma=0.1))
tpsa_over_100 = RdkitScoringFunction(descriptor=tpsa,
score_modifier=MaxGaussianModifier(
mu=100, sigma=10))
logP_scoring = RdkitScoringFunction(descriptor=logP,
score_modifier=MinGaussianModifier(
mu=1, sigma=1))
make_osimertinib_great_again = ArithmeticMeanScoringFunction([
similar_to_osimertinib, but_not_too_similar, tpsa_over_100,
logP_scoring
])
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Osimertinib MPO',
objective=make_osimertinib_great_again,
contribution_specification=specification)
def hard_cobimetinib() -> GoalDirectedBenchmark:
smiles = 'OC1(CN(C1)C(=O)C1=C(NC2=C(F)C=C(I)C=C2)C(F)=C(F)C=C1)C1CCCCN1'
modifier = ClippedScoreModifier(upper_x=0.7)
os_tf = TanimotoScoringFunction(smiles,
fp_type='FCFP4',
score_modifier=modifier)
os_ap = TanimotoScoringFunction(smiles,
fp_type='ECFP6',
score_modifier=MinGaussianModifier(
mu=0.75, sigma=0.1))
rot_b = RdkitScoringFunction(descriptor=num_rotatable_bonds,
score_modifier=MinGaussianModifier(mu=3,
sigma=1))
rings = RdkitScoringFunction(descriptor=num_aromatic_rings,
score_modifier=MaxGaussianModifier(mu=3,
sigma=1))
t_cns = ArithmeticMeanScoringFunction(
[os_tf, os_ap, rot_b, rings,
CNS_MPO_ScoringFunction()])
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Cobimetinib MPO',
objective=t_cns,
contribution_specification=specification)
def median_camphor_menthol(mean_cls=GeometricMeanScoringFunction) -> GoalDirectedBenchmark:
t_camphor = TanimotoScoringFunction('CC1(C)C2CCC1(C)C(=O)C2', fp_type='ECFP4')
t_menthol = TanimotoScoringFunction('CC(C)C1CCC(C)CC1O', fp_type='ECFP4')
median = mean_cls([t_menthol, t_camphor])
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Median molecules 1',
objective=median,
contribution_specification=specification)
def median_tadalafil_sildenafil() -> GoalDirectedBenchmark:
# median mol between tadalafil and sildenafil
m1 = TanimotoScoringFunction('O=C1N(CC(N2C1CC3=C(C2C4=CC5=C(OCO5)C=C4)NC6=C3C=CC=C6)=O)C', fp_type='ECFP6')
m2 = TanimotoScoringFunction('CCCC1=NN(C2=C1N=C(NC2=O)C3=C(C=CC(=C3)S(=O)(=O)N4CCN(CC4)C)OCC)C', fp_type='ECFP6')
median = GeometricMeanScoringFunction([m1, m2])
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Median molecules 2',
objective=median,
contribution_specification=specification)
def median_molecule() -> GoalDirectedBenchmark:
t_camphor = TanimotoScoringFunction('CC1(C)C2CCC1(C)C(=O)C2',
fp_type='ECFP4')
t_menthol = TanimotoScoringFunction('CC(C)C1CCC(C)CC1O', fp_type='ECFP4')
median = MultiPropertyScoringFunction([t_menthol, t_camphor])
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Median molecules',
objective=median,
contribution_specification=specification)
def similarity_cns_mpo(smiles, molecule_name, max_logP=5.0) -> GoalDirectedBenchmark:
benchmark_name = f'{molecule_name}'
os_tf = TanimotoScoringFunction(smiles, fp_type='FCFP4')
os_ap = TanimotoScoringFunction(smiles, fp_type='AP')
anti_fp = TanimotoScoringFunction(smiles, fp_type='ECFP6',
score_modifier=MinGaussianModifier(mu=0.70, sigma=0.1))
t_cns = ArithmeticMeanScoringFunction([os_tf, os_ap, anti_fp, CNS_MPO_ScoringFunction(max_logP=max_logP)])
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name=benchmark_name,
objective=t_cns,
contribution_specification=specification)
def scaffold_hop() -> GoalDirectedBenchmark:
"""
Keep the decoration, and similarity to start point, but change the scaffold.
"""
smiles = "CCCOc1cc2ncnc(Nc3ccc4ncsc4c3)c2cc1S(=O)(=O)C(C)(C)C"
pharmacophor_sim = TanimotoScoringFunction(
smiles,
fp_type="PHCO",
score_modifier=ClippedScoreModifier(upper_x=0.75))
deco = SMARTSScoringFunction(
"[#6]-[#6]-[#6]-[#8]-[#6]~[#6]~[#6]~[#6]~[#6]-[#7]-c1ccc2ncsc2c1",
inverse=False)
# anti scaffold
scaffold = SMARTSScoringFunction(
"[#7]-c1n[c;h1]nc2[c;h1]c(-[#8])[c;h0][c;h1]c12", inverse=True)
scaffold_hop_obj = ArithmeticMeanScoringFunction(
[pharmacophor_sim, deco, scaffold])
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name="Scaffold Hop",
objective=scaffold_hop_obj,
contribution_specification=specification)
def ranolazine_mpo() -> GoalDirectedBenchmark:
"""
Make start_pop_ranolazine more polar and add a fluorine
"""
ranolazine = "COc1ccccc1OCC(O)CN2CCN(CC(=O)Nc3c(C)cccc3C)CC2"
modifier = ClippedScoreModifier(upper_x=0.7)
similar_to_ranolazine = TanimotoScoringFunction(ranolazine,
fp_type="AP",
score_modifier=modifier)
logP_under_4 = RdkitScoringFunction(descriptor=logP,
score_modifier=MaxGaussianModifier(
mu=7, sigma=1))
tpsa_f = RdkitScoringFunction(descriptor=tpsa,
score_modifier=MaxGaussianModifier(mu=95,
sigma=20))
fluorine = RdkitScoringFunction(descriptor=AtomCounter("F"),
score_modifier=GaussianModifier(mu=1,
sigma=1.0))
optimize_ranolazine = GeometricMeanScoringFunction(
[similar_to_ranolazine, logP_under_4, fluorine, tpsa_f])
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(
name="Ranolazine MPO",
objective=optimize_ranolazine,
contribution_specification=specification,
starting_population=[ranolazine],
)
def sitagliptin_replacement() -> GoalDirectedBenchmark:
# Find a molecule dissimilar to sitagliptin, but with the same properties
smiles = "Fc1cc(c(F)cc1F)CC(N)CC(=O)N3Cc2nnc(n2CC3)C(F)(F)F"
sitagliptin = Chem.MolFromSmiles(smiles)
target_logp = logP(sitagliptin)
target_tpsa = tpsa(sitagliptin)
similarity = TanimotoScoringFunction(smiles,
fp_type="ECFP4",
score_modifier=GaussianModifier(
mu=0, sigma=0.1))
lp = RdkitScoringFunction(descriptor=logP,
score_modifier=GaussianModifier(mu=target_logp,
sigma=0.2))
tp = RdkitScoringFunction(descriptor=tpsa,
score_modifier=GaussianModifier(mu=target_tpsa,
sigma=5))
isomers = IsomerScoringFunction("C16H15F6N5O")
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(
name="Sitagliptin MPO",
objective=GeometricMeanScoringFunction([similarity, lp, tp, isomers]),
contribution_specification=specification,
)
def hard_fexofenadine() -> GoalDirectedBenchmark:
"""
make fexofenadine less greasy
:return:
"""
smiles = 'CC(C)(C(=O)O)c1ccc(cc1)C(O)CCCN2CCC(CC2)C(O)(c3ccccc3)c4ccccc4'
modifier = ClippedScoreModifier(upper_x=0.8)
similar_to_fexofenadine = TanimotoScoringFunction(smiles,
fp_type='AP',
score_modifier=modifier)
tpsa_over_90 = RdkitScoringFunction(descriptor=tpsa,
score_modifier=MaxGaussianModifier(
mu=90, sigma=10))
logP_under_4 = RdkitScoringFunction(descriptor=logP,
score_modifier=MinGaussianModifier(
mu=4, sigma=1))
optimize_fexofenadine = ArithmeticMeanScoringFunction(
[similar_to_fexofenadine, tpsa_over_90, logP_under_4])
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Fexofenadine MPO',
objective=optimize_fexofenadine,
contribution_specification=specification)
def start_pop_ranolazine() -> GoalDirectedBenchmark:
ranolazine = 'COc1ccccc1OCC(O)CN2CCN(CC(=O)Nc3c(C)cccc3C)CC2'
modifier = ClippedScoreModifier(upper_x=0.7)
similar_to_ranolazine = TanimotoScoringFunction(ranolazine,
fp_type='AP',
score_modifier=modifier)
logP_under_4 = RdkitScoringFunction(descriptor=logP,
score_modifier=MaxGaussianModifier(
mu=7, sigma=1))
aroma = RdkitScoringFunction(descriptor=num_aromatic_rings,
score_modifier=MinGaussianModifier(mu=1,
sigma=1))
fluorine = RdkitScoringFunction(descriptor=AtomCounter('F'),
score_modifier=GaussianModifier(mu=1,
sigma=1.0))
optimize_ranolazine = ArithmeticMeanScoringFunction(
[similar_to_ranolazine, logP_under_4, fluorine, aroma])
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Ranolazine MPO',
objective=optimize_ranolazine,
contribution_specification=specification,
starting_population=[ranolazine])
def zaleplon_with_other_formula() -> GoalDirectedBenchmark:
# zaleplon_with_other_formula with other formula
zaleplon = TanimotoScoringFunction('O=C(C)N(CC)C1=CC=CC(C2=CC=NC3=C(C=NN23)C#N)=C1',
fp_type='ECFP4')
formula = IsomerScoringFunction('C19H17N3O2')
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Zaleplon MPO',
objective=GeometricMeanScoringFunction([zaleplon, formula]),
contribution_specification=specification)
def amlodipine_rings() -> GoalDirectedBenchmark:
# amlodipine with 3 rings
amlodipine = TanimotoScoringFunction(r'Clc1ccccc1C2C(=C(/N/C(=C2/C(=O)OCC)COCCN)C)\C(=O)OC', fp_type='ECFP4')
rings = RdkitScoringFunction(descriptor=num_rings,
score_modifier=GaussianModifier(mu=3, sigma=0.5))
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Amlodipine MPO',
objective=GeometricMeanScoringFunction([amlodipine, rings]),
contribution_specification=specification)
def perindopril_rings() -> GoalDirectedBenchmark:
# perindopril with two aromatic rings
perindopril = TanimotoScoringFunction('O=C(OCC)C(NC(C(=O)N1C(C(=O)O)CC2CCCCC12)C)CCC',
fp_type='ECFP4')
arom_rings = RdkitScoringFunction(descriptor=num_aromatic_rings,
score_modifier=GaussianModifier(mu=2, sigma=0.5))
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Perindopril MPO',
objective=GeometricMeanScoringFunction([perindopril, arom_rings]),
contribution_specification=specification)
def similarity(smiles: str, name: str, fp_type: str = 'ECFP4', threshold: float = 0.7,
rediscovery: bool = False) -> GoalDirectedBenchmark:
category = 'rediscovery' if rediscovery else 'similarity'
benchmark_name = f'{name} {category}'
modifier = ClippedScoreModifier(upper_x=threshold)
scoring_function = TanimotoScoringFunction(target=smiles, fp_type=fp_type, score_modifier=modifier)
if rediscovery:
specification = uniform_specification(1)
else:
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name=benchmark_name,
objective=scoring_function,
contribution_specification=specification)
def similarity_constrained_penalized_logp_cyclebasis(smiles, name, threshold, dataset, fp_type="ECFP4"):
benchmark_name = f"{name} {threshold:.1f} Similarity Constrained Penalized logP"
objective = RdkitScoringFunction(descriptor=lambda mol: _penalized_logp_cyclebasis(mol, dataset))
offset = -objective.score(smiles)
constraint = TanimotoScoringFunction(target=smiles, fp_type=fp_type)
constrained_objective = ThresholdedImprovementScoringFunction(
objective=objective, constraint=constraint, threshold=threshold, offset=offset
)
constrained_objective.corrupt_score = -1000.0
specification = uniform_specification(1)
return GoalDirectedBenchmark(
name=benchmark_name, objective=constrained_objective, contribution_specification=specification
)
def pioglitazone_mpo() -> GoalDirectedBenchmark:
# pioglitazone with same mw but less rotatable bonds
smiles = 'O=C1NC(=O)SC1Cc3ccc(OCCc2ncc(cc2)CC)cc3'
pioglitazone = Chem.MolFromSmiles(smiles)
target_molw = mol_weight(pioglitazone)
similarity = TanimotoScoringFunction(smiles, fp_type='ECFP4',
score_modifier=GaussianModifier(mu=0, sigma=0.1))
mw = RdkitScoringFunction(descriptor=mol_weight,
score_modifier=GaussianModifier(mu=target_molw, sigma=10))
rb = RdkitScoringFunction(descriptor=num_rotatable_bonds,
score_modifier=GaussianModifier(mu=2, sigma=0.5))
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Pioglitazone MPO',
objective=GeometricMeanScoringFunction([similarity, mw, rb]),
contribution_specification=specification)
def decoration_hop() -> GoalDirectedBenchmark:
smiles = 'CCCOc1cc2ncnc(Nc3ccc4ncsc4c3)c2cc1S(=O)(=O)C(C)(C)C'
pharmacophor_sim = TanimotoScoringFunction(smiles, fp_type='PHCO',
score_modifier=ClippedScoreModifier(upper_x=0.85))
# change deco
deco1 = SMARTSScoringFunction('CS([#6])(=O)=O', inverse=True)
deco2 = SMARTSScoringFunction('[#7]-c1ccc2ncsc2c1', inverse=True)
# keep scaffold
scaffold = SMARTSScoringFunction('[#7]-c1n[c;h1]nc2[c;h1]c(-[#8])[c;h0][c;h1]c12', inverse=False)
deco_hop1_fn = ArithmeticMeanScoringFunction([pharmacophor_sim, deco1, deco2, scaffold])
specification = uniform_specification(1, 10, 100)
return GoalDirectedBenchmark(name='Deco Hop',
objective=deco_hop1_fn,
contribution_specification=specification)
