Python interface

This page gives a quick example of how the tree search can be completed by writing your own python interface. This is not recommended for most users.

  1. Import the necessary class

from aizynthfinder.aizynthfinder import AiZynthFinder

  1. Instantiate that class by providing a configuration file.

filename = "config.yml"
finder = AiZynthFinder(configfile=filename)

  1. Select stock and policy

finder.stock.select("zinc")
finder.expansion_policy.select("uspto")
finder.filter_policy.select("uspto")

zinc and uspto where the keys given to the stock and the policy in the configuration file. The first policy set is the expansion policy and the second is the filter policy. The filter policy is optional.

  1. Set the target SMILES and perform the tree search

finder.target_smiles = "Cc1cccc(c1N(CC(=O)Nc2ccc(cc2)c3ncon3)C(=O)C4CCS(=O)(=O)CC4)C"
finder.tree_search()

  1. Analyse the search tree and build routes

finder.build_routes()
stats = finder.extract_statistics()

The build_routes method needs to be called before any analysis can be done.

Expansion interface

There is an interface for the expansion policy as well. It can be used to break down a molecule into reactants.

filename = "config.yml"
expander = AiZynthExpander(configfile=filename)
expander.expansion_policy.select("uspto")
expander.filter_policy.select("uspto")
reactions = expander.do_expansion("Cc1cccc(c1N(CC(=O)Nc2ccc(cc2)c3ncon3)C(=O)C4CCS(=O)(=O)CC4)C")

for this, you only need to select the policies. The filter policy is optional and using it will only add the feasibility of the reactions not filter it out.

The result is a nested list of FixedRetroReaction objects. This you can manipulate to for instance get out all the reactants SMILES strings

reactants_smiles = []
for reaction_tuple in reactions:
    reactants_smiles.append([mol.smiles for mol in reaction_tuple[0].reactants[0])

or you can put all the metadata of all the reactions in a pandas dataframe

import pandas as pd
metadata = []
for reaction_tuple in reactions:
    for reaction in reaction_tuple:
        metadata.append(reaction.metadata)
df = pd.DataFrame(metadata)

Further reading

The docstrings of all modules, classes and methods can be consulted here

and you can always find them in an interactive Python shell using for instance:

from aizynthfinder.chem import Molecule
help(Molecule)
help(Molecule.fingerprint)

If you are interested in the the relationships between the classes have a look here and if you want to dig deeper in to the main algorithmic sequences have a look here