Source code for stk.ea.crossover.crossers.molecule.genetic_recombination

Genetic Recombination


import itertools as it
from collections import defaultdict

from ....molecule_records import MoleculeRecord
from ...records import CrossoverRecord
from .crosser import MoleculeCrosser

[docs]class GeneticRecombination(MoleculeCrosser): """ Recombine building blocks using biological systems as a model. Overall, this crosser mimics how animals and plants inherit DNA from their parents, except generalized to work with any number of parents. First it is worth discussing some terminology. A gene is a the smallest packet of genetic information. In animals, each gene can have multiple alleles. For example, there is a gene for hair color, and individual alleles for black, red, brown, etc. hair. This means that every person has a gene for hair color, but a person with black hair will have the black hair allele and a person with red hair will have the red hair allele. When two parents produce an offspring, the offspring will have a hair color gene and will inherit the allele of one of the parents at random. Therefore, if you have two parents, one with black hair and one with red hair, the offspring will either have black or red hair, depending on which allele they inherit. In an :mod:`.stk` :class:`.ConstructedMolecule`, each building block represents an allele. The question is, which gene is each building block an allele of? To answer that, let's first construct a couple of building block molecules .. testcode:: genetic-recombination import stk bb1 = stk.BuildingBlock( smiles='NCC(N)CN', functional_groups=[stk.PrimaryAminoFactory()], ) bb2 = stk.BuildingBlock('O=CCC=O', [stk.AldehydeFactory()]) bb3 = stk.BuildingBlock( smiles='O=CCNC(C=O)C=O', functional_groups=[stk.AldehydeFactory()], ) bb4 = stk.BuildingBlock( smiles='NCOCN', functional_groups=[stk.PrimaryAminoFactory()], ) We can define a function which analyzes a building block molecule and returns the gene it belongs to, for example .. testcode:: genetic-recombination def get_gene(building_block): fg, = building_block.get_functional_groups(0) return type(fg) .. testcode:: genetic-recombination :hide: assert get_gene(bb1) == stk.PrimaryAmino assert get_gene(bb2) == stk.Aldehyde assert get_gene(bb3) == stk.Aldehyde assert get_gene(bb4) == stk.PrimaryAmino Here, we can see that the gene, to which each building block molecule belongs, is given by the class of its first functional group. Therefore there is an :class:`.PrimaryAmino` gene, which has two alleles ``bb1`` and ``bb4``, and there is an :class:`.Aldehyde` gene, which has two alleles ``bb2`` and ``bb3``. Alternatively, we could have defined a function such as .. testcode:: genetic-recombination def get_gene2(building_block): return building_block.get_num_functional_groups() .. testcode:: genetic-recombination :hide: assert get_gene2(bb1) == 3 assert get_gene2(bb2) == 2 assert get_gene2(bb3) == 3 assert get_gene2(bb4) == 2 Now we can see that we end up with the gene called ``3``, which has two alleles ``bb1`` and ``bb3``, and a second gene called ``2``, which has the alleles ``bb2`` and ``bb4``. To produce offspring molecules, this class categorizes each building block of the parent molecules into genes using the `get_gene` parameter. Then, to generate a single offspring, it picks a building block for every gene. The picked building blocks are used to construct the offspring. The topology graph of the offspring is one of the parent's. For obvious reasons, this approach works with any number of parents. Examples -------- *Crossing Constructed Molecules* Note that any number of parents can be used for the crossover .. testcode:: crossing-constructed-molecules import stk # Create the molecule records which will be crossed. bb1 = stk.BuildingBlock('NCCN', [stk.PrimaryAminoFactory()]) bb2 = stk.BuildingBlock('O=CCCCC=O', [stk.AldehydeFactory()]) graph1 = stk.polymer.Linear((bb1, bb2), 'AB', 2) polymer1 = stk.ConstructedMolecule(graph1) record1 = stk.MoleculeRecord(graph1) bb3 = stk.BuildingBlock('NCCCN', [stk.PrimaryAminoFactory()]) bb4 = stk.BuildingBlock( smiles='O=C[Si]CCC=O', functional_groups=[stk.AldehydeFactory()], ) graph2 = stk.polymer.Linear((bb3, bb4), 'AB', 2) polymer2 = stk.ConstructedMolecule(graph2) record2 = stk.MoleculeRecord(graph2) # Create the crosser. def get_functional_group_type(building_block): fg, = building_block.get_functional_groups(0) return type(fg) recombination = stk.GeneticRecombination( get_gene=get_functional_group_type, ) # Get the offspring molecules. cohort1 = tuple(recombination.cross( records=(record1, record2), )) .. testcode:: crossing-constructed-molecules :hide: _expected_cohort = ( polymer1, polymer2, stk.ConstructedMolecule( topology_graph=stk.polymer.Linear( building_blocks=(bb1, bb4), repeating_unit='AB', num_repeating_units=2, ), ), stk.ConstructedMolecule( topology_graph=stk.polymer.Linear( building_blocks=(bb2, bb3), repeating_unit='AB', num_repeating_units=2, ), ), ) def _get_smiles(item): if isinstance(item, stk.ConstructedMolecule): return stk.Smiles().get_key(item) return stk.Smiles().get_key( molecule=item.get_molecule_record().get_molecule(), ) _expected_smiles = set(map(_get_smiles, _expected_cohort)) _cohort_smiles = set(map(_get_smiles, cohort1)) assert _expected_smiles == _cohort_smiles """
[docs] def __init__( self, get_gene, name="GeneticRecombination", ): """ Initialize a :class:`GeneticRecombination` instance. Parameters ---------- get_gene : :class:`callable` A :class:`callable`, which takes a :class:`.BuildingBlock` object and returns its gene. To produce an offspring, one of the building blocks from each gene is picked. name : :class:`str`, optional A name to identify the crosser instance. """ self._get_gene = get_gene self._name = name
[docs] def cross(self, records): topology_graphs = ( record.get_topology_graph() for record in records ) for topology_graph, alleles in it.product( topology_graphs, self._get_alleles(records), ): def get_replacement(building_block): gene = self._get_gene(building_block) return next( allele for allele in alleles if self._get_gene(allele) == gene ) topology_graph = topology_graph.with_building_blocks( building_block_map={ building_block: get_replacement(building_block) for building_block in topology_graph.get_building_blocks() }, ) yield CrossoverRecord( molecule_record=MoleculeRecord( topology_graph=topology_graph, ), crosser_name=self._name, )
def _get_alleles(self, records): """ Yield every possible combination of alleles. """ genes = defaultdict(list) topology_graphs = ( record.get_topology_graph() for record in records ) for topology_graph in topology_graphs: for allele in topology_graph.get_building_blocks(): genes[self._get_gene(allele)].append(allele) return it.product(*genes.values())