def make_monomer(length): """make an alkyl chain with two phenyl end caps >>> length = 7 >>> ap_monomer = AlkylPhenylPolymer.make_monomer(length) >>> ap_monomer.get_num_atoms() == 6 * 2 + length True >>> display(Draw.MolToImage(mol_with_atom_index(ap_monomer.to_rdkit_mol()), ... size=(700, 300))) """ alkane = init_building_block(smiles='F' + 'C' * length + 'F', functional_groups=[stk.FluoroFactory()]) benzene = init_building_block(smiles='FC1=CC=CC=C1', functional_groups=[stk.FluoroFactory()]) alkyl_phenyl = stk.ConstructedMolecule( topology_graph=stk.polymer.Linear( building_blocks=(alkane, benzene), repeating_unit='BAB', num_repeating_units=1, optimizer=stk.MCHammer(), )) return stk.BuildingBlock.init_from_molecule(alkyl_phenyl)
hydrogen=stk.H(5), atom=stk.C(1), bonders=(stk.S(0), ), deleters=(stk.H(5), ), ), stk.Thiol( sulfur=stk.S(4), hydrogen=stk.H(12), atom=stk.C(3), bonders=(stk.S(4), ), deleters=(stk.H(12), ), ), ), ), lambda: CaseData( factory=stk.FluoroFactory(), molecule=stk.BuildingBlock('FCC(F)CCF'), functional_groups=( stk.Fluoro( fluorine=stk.F(0), atom=stk.C(1), bonders=(stk.C(1), ), deleters=(stk.F(0), ), ), stk.Fluoro( fluorine=stk.F(3), atom=stk.C(2), bonders=(stk.C(2), ), deleters=(stk.F(3), ), ), stk.Fluoro(
55, 56, 57, 58, 59, 31, 62, 63, ), stk.BuildingBlock( smiles=('Br[C+]1[C+]2[N+][C+2]C2(Br)[C+](I)[C+' '](I)[C+](Br)[C+]1Br'), functional_groups=[ stk.BromoFactory(), stk.IodoFactory(), stk.FluoroFactory(), ], ): (0, 1, 18, 50, 51), stk.BuildingBlock( smiles=('Br[C+]1[C+]2[S][C+2]C2(Br)[C+](I)[C+]' '(I)[C+](Br)[C+]1Br'), functional_groups=[ stk.BromoFactory(), stk.IodoFactory(), stk.FluoroFactory(), ], ): (2, 16, 34, 49), stk.BuildingBlock( smiles=('Br[C+]1[C+]2[S][O]C2(Br)[C+](I)[C+](I' ')[C+](Br)[C+]1Br'), functional_groups=[
def main(): parser = argparse.ArgumentParser() parser.add_argument('--mongodb_uri', help='The MongoDB URI for the database to connect to.', default='mongodb://localhost:27017/') args = parser.parse_args() logging.basicConfig(level=logging.INFO) # Use a random seed to get reproducible results. random_seed = 4 generator = np.random.RandomState(random_seed) logger.info('Making building blocks.') # Load the building block databases. fluoros = tuple( get_building_blocks( path=pathlib.Path(__file__).parent / 'fluoros.txt', functional_group_factory=stk.FluoroFactory(), )) bromos = tuple( get_building_blocks( path=pathlib.Path(__file__).parent / 'bromos.txt', functional_group_factory=stk.BromoFactory(), )) initial_population = tuple(get_initial_population(fluoros, bromos)) # Write the initial population. for i, record in enumerate(initial_population): write(record.get_molecule(), f'initial_{i}.mol') client = pymongo.MongoClient(args.mongodb_uri) db = stk.ConstructedMoleculeMongoDb(client) fitness_db = stk.ValueMongoDb(client, 'fitness_values') # Plot selections. generation_selector = stk.Best( num_batches=25, duplicate_molecules=False, ) stk.SelectionPlotter('generation_selection', generation_selector) mutation_selector = stk.Roulette( num_batches=5, random_seed=generator.randint(0, 1000), ) stk.SelectionPlotter('mutation_selection', mutation_selector) crossover_selector = stk.Roulette( num_batches=3, batch_size=2, random_seed=generator.randint(0, 1000), ) stk.SelectionPlotter('crossover_selection', crossover_selector) fitness_calculator = stk.PropertyVector( property_functions=( get_num_rotatable_bonds, get_complexity, get_num_bad_rings, ), input_database=fitness_db, output_database=fitness_db, ) fitness_normalizer = stk.NormalizerSequence( fitness_normalizers=( # Prevent division by 0 error in DivideByMean, by ensuring # a value of each property to be at least 1. stk.Add((1, 1, 1)), stk.DivideByMean(), # Obviously, because all coefficients are equal, the # Multiply normalizer does not need to be here. However, # it's here to show that you can easily change the relative # importance of each component of the fitness value, by # changing the values of the coefficients. stk.Multiply((1, 1, 1)), stk.Sum(), stk.Power(-1), ), ) ea = stk.EvolutionaryAlgorithm( num_processes=1, initial_population=initial_population, fitness_calculator=fitness_calculator, mutator=stk.RandomMutator( mutators=( stk.RandomBuildingBlock( building_blocks=fluoros, is_replaceable=is_fluoro, random_seed=generator.randint(0, 1000), ), stk.SimilarBuildingBlock( building_blocks=fluoros, is_replaceable=is_fluoro, random_seed=generator.randint(0, 1000), ), stk.RandomBuildingBlock( building_blocks=bromos, is_replaceable=is_bromo, random_seed=generator.randint(0, 1000), ), stk.SimilarBuildingBlock( building_blocks=bromos, is_replaceable=is_bromo, random_seed=generator.randint(0, 1000), ), ), random_seed=generator.randint(0, 1000), ), crosser=stk.GeneticRecombination(get_gene=get_functional_group_type, ), generation_selector=generation_selector, mutation_selector=mutation_selector, crossover_selector=crossover_selector, fitness_normalizer=fitness_normalizer, ) logger.info('Starting EA.') generations = [] for generation in ea.get_generations(50): for record in generation.get_molecule_records(): db.put(record.get_molecule()) generations.append(generation) # Write the final population. for i, record in enumerate(generation.get_molecule_records()): write(record.get_molecule(), f'final_{i}.mol') logger.info('Making fitness plot.') # Normalize the fitness values across the entire EA before # plotting the fitness values. generations = tuple( normalize_generations( fitness_calculator=fitness_calculator, fitness_normalizer=fitness_normalizer, generations=generations, )) fitness_progress = stk.ProgressPlotter( generations=generations, get_property=lambda record: record.get_fitness_value(), y_label='Fitness Value', ) fitness_progress.write('fitness_progress.png') fitness_progress.get_plot_data().to_csv('fitness_progress.csv') logger.info('Making rotatable bonds plot.') rotatable_bonds_progress = stk.ProgressPlotter( generations=generations, get_property=lambda record: get_num_rotatable_bonds(record. get_molecule()), y_label='Number of Rotatable Bonds', ) rotatable_bonds_progress.write('rotatable_bonds_progress.png')
def main(): parser = argparse.ArgumentParser() parser.add_argument( '--mongodb_uri', help='The MongoDB URI for the database to connect to.', default='mongodb://localhost:27017/', ) args = parser.parse_args() logging.basicConfig(level=logging.INFO) # Use a random seed to get reproducible results. random_seed = 4 generator = np.random.RandomState(random_seed) logger.info('Making building blocks.') # Load the building block databases. fluoros = tuple( get_building_blocks( path=pathlib.Path(__file__).parent / 'fluoros.txt', functional_group_factory=stk.FluoroFactory(), )) bromos = tuple( get_building_blocks( path=pathlib.Path(__file__).parent / 'bromos.txt', functional_group_factory=stk.BromoFactory(), )) initial_population = tuple(get_initial_population(fluoros, bromos)) # Write the initial population. for i, record in enumerate(initial_population): write(record.get_molecule(), f'initial_{i}.mol') client = pymongo.MongoClient(args.mongodb_uri) db = stk.ConstructedMoleculeMongoDb(client) ea = stk.EvolutionaryAlgorithm( initial_population=initial_population, fitness_calculator=stk.FitnessFunction(get_fitness_value), mutator=stk.RandomMutator( mutators=( stk.RandomBuildingBlock( building_blocks=fluoros, is_replaceable=is_fluoro, random_seed=generator.randint(0, 1000), ), stk.SimilarBuildingBlock( building_blocks=fluoros, is_replaceable=is_fluoro, random_seed=generator.randint(0, 1000), ), stk.RandomBuildingBlock( building_blocks=bromos, is_replaceable=is_bromo, random_seed=generator.randint(0, 1000), ), stk.SimilarBuildingBlock( building_blocks=bromos, is_replaceable=is_bromo, random_seed=generator.randint(0, 1000), ), ), random_seed=generator.randint(0, 1000), ), crosser=stk.GeneticRecombination(get_gene=get_functional_group_type, ), generation_selector=stk.Best( num_batches=25, duplicate_molecules=False, ), mutation_selector=stk.Roulette( num_batches=5, random_seed=generator.randint(0, 1000), ), crossover_selector=stk.Roulette( num_batches=3, batch_size=2, random_seed=generator.randint(0, 1000), ), ) logger.info('Starting EA.') generations = [] for generation in ea.get_generations(50): for record in generation.get_molecule_records(): db.put(record.get_molecule()) generations.append(generation) # Write the final population. for i, record in enumerate(generation.get_molecule_records()): write(record.get_molecule(), f'final_{i}.mol') logger.info('Making fitness plot.') fitness_progress = stk.ProgressPlotter( generations=generations, get_property=lambda record: record.get_fitness_value(), y_label='Fitness Value', ) fitness_progress.write('fitness_progress.png') logger.info('Making rotatable bonds plot.') rotatable_bonds_progress = stk.ProgressPlotter( generations=generations, get_property=get_num_rotatable_bonds, y_label='Number of Rotatable Bonds', ) rotatable_bonds_progress.write('rotatable_bonds_progress.png')