Unsupervised attention-guided atom-mapping

Installation instructions

From Conda

From Pip

Unfortunately, rdkit is not supported on pip, which makes this a slightly more involved process.

From github

conda create -n rxnmapper python=3.6 -y
conda activate rxnmapper
conda install -c rdkit rdkit
pip install git+https://github.ibm.com/PHS/rxnmapper.git

For development

You can install the environment for local development using the following:

conda env create -f environment.yml
conda activate rxnmapper
pip install -e .
pip install -r requirements.txt

Usage

Basic usage

from rxnmapper import RXNMapper
rxn_mapper = RXNMapper()
rxns = ['CC(C)S.CN(C)C=O.Fc1cccnc1F.O=C([O-])[O-].[K+].[K+]>>CC(C)Sc1ncccc1F', 'C1COCCO1.CC(C)(C)OC(=O)CONC(=O)NCc1cccc2ccccc12.Cl>>O=C(O)CONC(=O)NCc1cccc2ccccc12']
results = rxn_mapper.get_attention_guided_atom_maps(rxns)

The results contain the mapped reactions and confidence scores:

[{'mapped_rxn': 'CN(C)C=O.F[c:5]1[n:6][cH:7][cH:8][cH:9][c:10]1[F:11].O=C([O-])[O-].[CH3:1][CH:2]([CH3:3])[SH:4].[K+].[K+]>>[CH3:1][CH:2]([CH3:3])[S:4][c:5]1[n:6][cH:7][cH:8][cH:9][c:10]1[F:11]',
  'confidence': 0.9565619900376546},
 {'mapped_rxn': 'C1COCCO1.CC(C)(C)[O:3][C:2](=[O:1])[CH2:4][O:5][NH:6][C:7](=[O:8])[NH:9][CH2:10][c:11]1[cH:12][cH:13][cH:14][c:15]2[cH:16][cH:17][cH:18][cH:19][c:20]12.Cl>>[O:1]=[C:2]([OH:3])[CH2:4][O:5][NH:6][C:7](=[O:8])[NH:9][CH2:10][c:11]1[cH:12][cH:13][cH:14][c:15]2[cH:16][cH:17][cH:18][cH:19][c:20]12',
  'confidence': 0.9704424331552834}]

Testing

You can run the examples above with the test suite as well:

pip install -r dev_requirements.txt
pytest tests from the root

Examples

To learn more see the examples.

Data

Data can be found at: https://ibm.box.com/v/RXNMapperData

Metadata

Release files for rxnmapper 0.1.1

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Source distribution (sdist)

Source distribution for rxnmapper 0.1.1
File	Size	Uploaded	Provenance
rxnmapper-0.1.1.tar.gz	3.0 MB	May 14, 2020	Without provenance	Details

Release files / rxnmapper-0.1.1.tar.gz

Download URL	rxnmapper-0.1.1.tar.gz
Size	3.0 MB
Tags	Source
SHA-256 checksum How to use checksums	`96c915e40944f75766d0e93fbf1bcf2790f32332a1e6df2e76f31c231ccc1912`
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Uploaded via	`twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/46.0.0.post20200309 requests-toolbelt/0.9.1 tqdm/4.35.0 CPython/3.6.10`

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