m1-api-wrapper 2.1.1

Molecule One API Wrapper

Molecule One Batch Scoring API Wrapper

Usage:

Installation:

pip install git+https://github.com/molecule-one/m1wrapper-python

Initialization:

from m1wrapper import MoleculeOneWrapper
m1wrapper = MoleculeOneWrapper(api_token, 'https://app.molecule.one')

• api_token: API token you'll need to authorize in our system. You can generate yours at https://app.molecule.one/dashboard/user/api-tokens
• api_base_url (optional): URI of the batch scoring service. Defaults to Molecule One's public server, but you will need to provide custom value if you're using a dedicated solution.

Getting a list of batch scoring requests:

searches = m1wrapper.list_batch_searches()

Running new batch scoring request:

search = m1wrapper.run_batch_search(
    targets=['cc', 'O=C(Nc1cc(Nc2nc(-c3cnccc3)ccn2)c(cc1)C)c3ccc(cc3)CN3CCN(CC3)C'],
    parameters={'model': 'gat'}
)

• targets: list of target compounds in SMILES format
• parameters (optional): additional configuration for your batch scoring request. See Batch Scoring API for more information.
• detail_level (optional): detail level of the batch request
• priority (optional): priority of the batch request
• invalid_target_strategy (optional): if set to InvalidTargetStrategy.PASS, targets that cannot be canonized by our SMILES parser won't cause the whole batch request to be rejected. Defaults to InvalidTargetStrategy.REJECT.
• starting_materials (optional): list of available compounds in SMILES format
• name (optional): name of your batch request

Batch scoring detail level

Detail level determines how much information about each target synthesis you'll get. We define it as a DetailLevel enum with two variants:

• DetailLevel.SCORE (default) - useful when you're not interested in full synthesis json/UI preview, but only numerical values
• DetailLevel.SYNTHESIS - when you're also interested in reactions and compounds leading to the target product

Example:

from m1wrapper import MoleculeOneWrapper, DetailLevel
m1wrapper = MoleculeOneWrapper(api_token, 'https://app.molecule.one')
search = m1wrapper.run_batch_search(
    targets=['cc', 'O=C(Nc1cc(Nc2nc(-c3cnccc3)ccn2)c(cc1)C)c3ccc(cc3)CN3CCN(CC3)C'],
    parameters={'model': 'gat', },
    detail_level=DetailLevel.SCORE
)

Batch scoring priorities

Priorities are defined as integers in a range of 1 to 10. Requests with higher priority will be processed before those with lower priority. For convenience, we also define a Priority enum with the following variants:

• Priority.LOWEST (1)
• Priority.LOW (3)
• Priority.NORMAL (5, default)
• Priority.HIGH (8)
• Priority.HIGHEST (10)

Example:

from m1wrapper import MoleculeOneWrapper, Priority
m1wrapper = MoleculeOneWrapper(api_token, 'https://app.molecule.one')
search = m1wrapper.run_batch_search(
    targets=['cc', 'O=C(Nc1cc(Nc2nc(-c3cnccc3)ccn2)c(cc1)C)c3ccc(cc3)CN3CCN(CC3)C'],
    parameters={'model': 'gat'},
    priority=Priority.HIGH
)

Batch scoring request with compound metadata

run_batch_search_with_metadata(targets_with_metadata, parameters, detail_level, priority, invalid_target_strategy, starting_materials, name)

• targets_with_metadata: list of target compounds with metadata. Each target compound should be a dictionary object of shape { 'smiles': str, 'name': str} where the only required field is smiles.
• parameters (optional): additional configuration for your batch scoring request. See Batch Scoring API for more information.
• detail_level (optional): detail level of the batch request
• priority (optional): priority of the batch request
• invalid_target_strategy (optional): if set to InvalidTargetStrategy.PASS, targets that cannot be canonized by our SMILES parser won't cause the whole batch request to be rejected. Defaults to InvalidTargetStrategy.REJECT.
• starting_materials (optional): list of available compounds in SMILES format
• name (optional): name of your batch request

run_batch_search_with_metadata(
  targets_with_metadata=[{'name': 'compound1', 'smiles': 'cc'}, {'smiles': 'O=C(Nc1cc(Nc2nc(-c3cnccc3)ccn2)c(cc1)C)c3ccc(cc3)CN3CCN(CC3)C'}, {'name': 'compound3', 'smiles': 'CC'}],
  priority=Priority.HIGH
)

Getting exisiting scoring request by id:

search = m1wrapper.get_batch_search(id)

Checking if your scoring request processing is finished:

search.is_finished()

Checking full search status:

status = search.get_status()

In response, you’ll get information about your batch scoring processing progress, i.e.: {"queued":92,"running":4,"finished":104,"error":0}

Getting partial results:

Results are made available as soon as they are processed. This method provided a way to start working with some of your results without waiting until all targets are processed. This usually means implementing some kind of polling/scheduling on your side.

results = search.get_partial_results(precision=5, only=["target_smiles", "result"])

• precision (optional): format the floating point scores returned by the system (certainty, result, price) to given number of significant digits.
• only (optional): fetch only a subset of values. Defaults to all values.

Returns JSON object of the following shape: Returns an object of the following shape:

    [
      {
        'target_smiles': 'Cc1ccc(cc1Nc2nccc(n2)c3cccnc3)NC(=O)c4ccc(cc4)CN5CCN(CC5)C',
        'result': '7.53000'
      },
      ...
    ]

All values:

results = search.get_partial_results(precision=5)

Returns JSON object of the following shape:

    [
      {
        'target_smiles': 'Cc1ccc(cc1Nc2nccc(n2)c3cccnc3)NC(=O)c4ccc(cc4)CN5CCN(CC5)C',
        'status': 'ok',
        'result': '7.53000',
        'certainty': '0.581',
        'price': '5230',
        'reaction_count': 5,
        'timed_out': False,
        'started_at': '2021-09-13T14:45:31.012Z',
        'finished_at': '2021-09-13T14:46:39.199Z',
        'running_time': 68.187,
        'url': 'https://app.molecule.one/dashboard/synthesis-plans/batch/b787bf5f-6736-443c-bef1-8f10a37da246/result/0e3c6e13-fce1-46ba-9811-8fe66e0e4122'
      },
      ...
    ]

See Batch Scoring API for a full explaination of returned fields.

Getting complete results:

results = search.get_results(precision=5, only=["target_smiles", "result"])

If you don't want to implement scheduling on your own, this method provides a simple way to wait until all targets are processed (sending periodical checks using search.is_finished()), and execute only when all results are available. It's a blocking operation. Parameters and returned JSON are the same as with get_partial_results().

Deleting your data:

m1wrapper.delete_batch_search(search.search_id)