def test_arithmetic_mean_scoring_function(): # define a scoring function returning the mean from two mock functions # and assert that it returns the correct values. weight_1 = 0.4 weight_2 = 0.6 mock_values_1 = [0.232, 0.665, 0.0, 1.0, 0.993] mock_values_2 = [0.010, 0.335, 0.8, 0.3, 0.847] mock_1 = MockScoringFunction(mock_values_1) mock_2 = MockScoringFunction(mock_values_2) scoring_function = ArithmeticMeanScoringFunction( scoring_functions=[mock_1, mock_2], weights=[weight_1, weight_2]) smiles = ['CC'] * 5 scores = scoring_function.score_list(smiles) expected = [ weight_1 * v1 + weight_2 * v2 for v1, v2 in zip(mock_values_1, mock_values_2) ] assert scores == expected
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 weird_physchem() -> GoalDirectedBenchmark: min_bertz = RdkitScoringFunction(descriptor=bertz, score_modifier=MaxGaussianModifier( mu=1500, sigma=200)) mol_under_400 = RdkitScoringFunction(descriptor=mol_weight, score_modifier=MinGaussianModifier( mu=400, sigma=40)) aroma = RdkitScoringFunction(descriptor=num_aromatic_rings, score_modifier=MinGaussianModifier(mu=3, sigma=1)) fluorine = RdkitScoringFunction(descriptor=AtomCounter('F'), score_modifier=GaussianModifier(mu=6, sigma=1.0)) opt_weird = ArithmeticMeanScoringFunction( [min_bertz, mol_under_400, aroma, fluorine]) specification = uniform_specification(1, 10, 100) return GoalDirectedBenchmark(name='Physchem MPO', objective=opt_weird, 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 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 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 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 = ArithmeticMeanScoringFunction([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 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)