https://github.com/rxn4chemistry/rxnmapper

RXNMapper: Unsupervised attention-guided atom-mapping. Code complementing our Science Advances publication on "Extraction of organic chemistry grammar from unsupervised learning of chemical reactions" (https://advances.sciencemag.org/content/7/15/eabe4166).
https://github.com/rxn4chemistry/rxnmapper

atom-mapping chemistry reactions rxn smiles transformer

Last synced: 25 days ago
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RXNMapper: Unsupervised attention-guided atom-mapping. Code complementing our Science Advances publication on "Extraction of organic chemistry grammar from unsupervised learning of chemical reactions" (https://advances.sciencemag.org/content/7/15/eabe4166).

• Host: GitHub
• URL: https://github.com/rxn4chemistry/rxnmapper
• Owner: rxn4chemistry
• License: mit
• Created: 2020-05-13T19:01:05.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2024-01-25T08:27:46.000Z (5 months ago)
• Last Synced: 2024-05-27T12:00:57.326Z (about 1 month ago)
• Topics: atom-mapping, chemistry, reactions, rxn, smiles, transformer
• Language: Python
• Homepage: http://rxnmapper.ai
• Size: 8.41 MB
• Stars: 269
• Watchers: 11
• Forks: 62
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-biochem-ai - RXNMapper (2021) - based encoder-decoder transformers to perform atom-atom mapping between two SMILES strings. (Atom Mapping)

README

        
# Extraction of organic chemistry grammar from unsupervised learning of chemical reactions

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.

- [Extraction of organic chemistry grammar from unsupervised learning of chemical reactions](https://advances.sciencemag.org/content/7/15/eabe4166): peer-reviewed Science Advances publication (open access).

- [Demo](http://rxnmapper.ai/demo.html): give RXNMapper a try! 

- [Unsupervised attention-guided atom-mapping preprint](http://dx.doi.org/10.26434/chemrxiv.12298559): presented at the ML Interpretability for Scientific Discovery ICML workshop, 2020.

## Installation

### From pip

```console

conda create -n rxnmapper python=3.6 -y

conda activate rxnmapper

pip install rxnmapper

```

### From github

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

```console

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

cd rxnmapper

conda create -n rxnmapper python=3.6 -y

conda activate rxnmapper

pip install -e .

```

### RDkit

In both installation settings above, the `RDKit` dependency is not installed automatically, unless you include the extra when installing: `pip install "rxmapper[rdkit]"`.

It can also be installed via Conda or Pypi:

```bash

# Install RDKit from Conda

conda install -c conda-forge rdkit

# Install RDKit from Pypi

pip install rdkit

# for Python<3.7

# pip install rdkit-pypi

```

## Usage

### Basic usage

```python

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:

```python

[{'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}]

```

To account for batching and error handling automatically, you can use `BatchedMapper` instead:

```python

from rxnmapper import BatchedMapper

rxn_mapper = BatchedMapper(batch_size=32)

rxns = ['CC[O-]~[Na+].BrCC>>CCOCC', 'invalid>>reaction']

# The following calls work with input of arbitrary size. Also, they do not raise 

# any exceptions but will return ">>" or an empty dictionary for the second reaction.

results = list(rxn_mapper.map_reactions(rxns))  # results as strings directly

results = list(rxn_mapper.map_reactions_with_info(rxns))  # results as dictionaries (as above)

```

### Testing

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

1. In your Conda environment: `pip install -e .[dev]`

2. `pytest tests` from the root 

## Examples

To learn more see the [examples](./examples).

## Data 

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

## Citation

```

@article{schwaller2021extraction,

  title={Extraction of organic chemistry grammar from unsupervised learning of chemical reactions},

  author={Schwaller, Philippe and Hoover, Benjamin and Reymond, Jean-Louis and Strobelt, Hendrik and Laino, Teodoro},

  journal={Science Advances},

  volume={7},

  number={15},

  pages={eabe4166},

  year={2021},

  publisher={American Association for the Advancement of Science}

}

```