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chemical reaction fingerprints

Project description

RXNFP - chemical reaction fingerprints

This library generates chemical reaction fingerprints from reaction SMILES

Install

For all installations, we recommend using conda to get the necessary rdkit and tmap dependencies:

From pypi

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

From github

conda create -n rxnfp python=3.6 -y
conda activate rxnfp
conda install -c rdkit rdkit
conda install -c tmap tmap
git clone git@github.com:rxn4chemistry/rxnfp.git
cd rxnfp
pip install -e .

How to use

Compute a fingerprint from a reaction SMILES

</code></pre>
<pre><code>from rxnfp.transformer_fingerprints import (
    RXNBERTFingerprintGenerator, get_default_model_and_tokenizer, generate_fingerprints
)

model, tokenizer = get_default_model_and_tokenizer()

rxnfp_generator = RXNBERTFingerprintGenerator(model, tokenizer)

example_rxn = "Nc1cccc2cnccc12.O=C(O)c1cc([N+](=O)[O-])c(Sc2c(Cl)cncc2Cl)s1>>O=C(Nc1cccc2cnccc12)c1cc([N+](=O)[O-])c(Sc2c(Cl)cncc2Cl)s1"

fp = rxnfp_generator.convert(example_rxn)
print(len(fp))
print(fp[:5])
256
[-2.0174953937530518, 1.7602033615112305, -1.3323537111282349, -1.1095019578933716, 1.2254549264907837]

Or for a list of reactions:

rxns = [example_rxn, example_rxn]
fps = rxnfp_generator.convert_batch(rxns)
print(len(fps), len(fps[0]))
2 256

Reaction Atlas

Pistachio

The fingerprints can be used to map the space of chemical reactions:

Figure: Annotated Atlas of the Pistachio test set generated with TMAP.

Schneider 50k set - tutorial

In the notebooks, we show how to generate an interative reaction atlas for the Schneider 50k set. The end result is similar to this interactive Reaction Atlas.

Where you will find different reaction properties highlighted in the different layers:

Figure: Reaction atlas of 50k data set with different properties highlighted.

Citation

Our work was first presented in the NeurIPS 2019 workshop for Machine Learning and the Physical Sciences. The most recent version of our preprint can be found on ChemRxiv.

@article{Schwaller2019rxnfp,
author = "Philippe Schwaller and Daniel Probst and Alain C. Vaucher and Vishnu H Nair and David Kreutter and Teodoro Laino and Jean-Louis Reymond",
title = "{Mapping the Space of Chemical Reactions using Attention-Based Neural Networks}",
year = "2019",
month = "9",
url = "https://chemrxiv.org/articles/preprint/Data-Driven_Chemical_Reaction_Classification_with_Attention-Based_Neural_Networks/9897365",
doi = "10.26434/chemrxiv.9897365.v3"
}

RXNFP has been developed in a collaboration between IBM Research Europe and the Reymond group at the University of Bern. The classification models are used on the RXN for Chemistry platform.

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