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 github
conda create -n rxnfp python=3.6 -y
conda activate rxnfp
conda install -c rdkit rdkit
conda install -c tmap tmap
pip install -e .
How to use
Compute a fingerprint from a reaction SMILES
</code></pre>
<pre lang="python"><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 (https://tmap.gdb.tools).
Schneider 50k set
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
@article{Schwaller2019rxnfp,
author = "Philippe Schwaller and Daniel Probst and Alain C. Vaucher and Vishnu H Nair and Teodoro Laino and Jean-Louis Reymond",
title = "{Data-Driven Chemical Reaction Classification, Fingerprinting and Clustering using Attention-Based Neural Networks}",
year = "2019",
month = "12",
url = "https://chemrxiv.org/articles/preprint/Data-Driven_Chemical_Reaction_Classification_with_Attention-Based_Neural_Networks/9897365",
doi = "10.26434/chemrxiv.9897365.v2"
}
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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