Metadata-Version: 2.1
Name: rxnfp
Version: 0.0.8
Summary: chemical reaction fingerprints
Home-page: https://github.com/rxn4chemistry/rxnfp/tree/master/
Author: Philippe Schwaller
Author-email: phs@zurich.ibm.com
License: MIT License
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
        
        ```console
        conda create -n rxnfp python=3.6 -y
        conda activate rxnfp
        conda install -c rdkit rdkit=2020.03.3 -y
        conda install -c tmap tmap -y
        pip install rxnfp
        ```
        
        
        ### From github
        ```console
        conda create -n rxnfp python=3.6 -y
        conda activate rxnfp
        conda install -c rdkit rdkit=2020.03.3 -y
        conda install -c tmap tmap -y
        git clone git@github.com:rxn4chemistry/rxnfp.git
        cd rxnfp
        pip install -e .
        ```
        
        ## How to use
        
        Compute a fingerprint from a reaction SMILES
        ```python
        ```
        
        ```python
        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:
        
        ```python
        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:
        
        
        <div style="text-align: center">
        <img src="nbs/images/annotated_atlas.jpg" width="1000">
        <p style="text-align: center;"> <b>Figure:</b> Annotated Atlas of the Pistachio test set generated with <a href="https://tmap.gdb.tools">TMAP</a>. </p>
        </div>
        
        
        ### 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](https://rxn4chemistry.github.io/rxnfp//tmaps/tmap_ft_10k.html)**.
        
        Where you will find different reaction properties highlighted in the different layers:
        
        <div style="text-align: center">
        <img src="nbs/images/tmap_properties.jpg" width="800">
        <p style="text-align: center;"> <b>Figure:</b> Reaction atlas of 50k data set with different properties highlighted. </p>
        </div>
        
        ## USPTO 1k TPL (reaction classification data set)
        
        We introduce a new data set for chemical reaction classification called USPTO 1k TPL. USPTO 1k TPL is derived from the [USPTO data base](https://figshare.com/articles/Chemical_reactions_from_US_patents_1976-Sep2016_/5104873) by Lowe. It consists of 445k reactions divided into 1000 template labels. The data set was randomly split into train/valid 90% and test 10%. The labels were obtained by atom-mapping the USPTO data set with [RXNMapper](http://rxnmapper.ai), then applying the [template extraction workflow](https://github.com/reymond-group/CASP-and-dataset-performance) by Thakkar et al. and finally, selecting reactions belonging to the 1000 most frequent template hashes. Those template hashes were taken as class labels. Similarly to the Pistachio data set, USPTO 1k TPL is strongly imbalanced.
        
        The data set can be downloaded from: [MappingChemicalReactions](https://ibm.box.com/v/MappingChemicalReactions).
        
        ## Citation 
        
        Our work was first presented in the NeurIPS 2019 workshop for [Machine Learning and the Physical Sciences](https://ml4physicalsciences.github.io). The most recent version of our preprint can be found on [ChemRxiv](https://chemrxiv.org/articles/Data-Driven_Chemical_Reaction_Classification_with_Attention-Based_Neural_Networks/9897365).
        
        ```
        @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](http://gdb.unibe.ch) at the University of Bern. The classification models are used on the [RXN for Chemistry](https://rxn.res.ibm.com) platform.
        
Keywords: chemical reactions fingerprints classification
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
