rxnmapper 0.1.2

Reaction atom-mapping from transfomers

Unsupervised attention-guided atom-mapping

Enable robust atom mapping on valid reaction SMILES. The atom-mapping information was learned by an ALBERT model trained in an unsupervised fashion on a large dataset of chemical reactions.

This software is accompanied by a demo and a writeup, all accesible at http://rxnmapper.ai.

Documentation.

Installation

For all installations, we recommend using conda to get the necessary rdkit dependency:

From pip

conda create -n rxnmapper python=3.6 -y
conda activate rxnmapper
conda install -c rdkit rdkit
pip install rxnmapper

From github

You can install the package and setup the environment directly from github using:

git clone https://github.com/rxn4chemistry/rxnmapper.git 
cd rxnmapper
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:

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

Examples

To learn more see the examples.

Data

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