gt4sd.algorithms.conditional_generation.guacamol.implementation.smiles_ga module¶

SMILES GA implementation.

Summary¶

Classes:

class SMILESGA(smi_file, population_size, n_mutations, n_jobs, random_start, gene_size, generations, patience)[source]¶

Bases: object

__init__(smi_file, population_size, n_mutations, n_jobs, random_start, gene_size, generations, patience)[source]¶

Initialize SMILESGA.

Parameters

smi_file – path where to load hypothesis, candidate labels and, optionally, the smiles file.
population_size (int) – used with n_mutations for the initial generation of smiles within the population.
n_mutations (int) – used with population size for the initial generation of smiles within the population.
n_jobs (int) – number of concurrently running jobs.
random_start (bool) – set to True to randomly choose list of SMILES for generating optimizied molecules.
gene_size (int) – size of the gene which is used in creation of genes.
generations (int) – number of evolutionary generations.
patience (int) – used for early stopping if population scores remains the same after generating molecules.

get_generator()[source]¶

Create an instance of ChemGEGenerator.

__dict__ = mappingproxy({'__module__': 'gt4sd.algorithms.conditional_generation.guacamol.implementation.smiles_ga', '__init__': <function SMILESGA.__init__>, 'get_generator': <function SMILESGA.get_generator>, '__dict__': <attribute '__dict__' of 'SMILESGA' objects>, '__weakref__': <attribute '__weakref__' of 'SMILESGA' objects>, '__doc__': None, '__annotations__': {}})¶

__module__ = 'gt4sd.algorithms.conditional_generation.guacamol.implementation.smiles_ga'¶