Metadata-Version: 2.1
Name: drfp
Version: 0.2.1
Summary: An NLP-inspired chemical reaction fingerprint based on basic set arithmetic.
Home-page: https://github.com/daenuprobst/drfp
Author: Daniel Probst
Author-email: daniel.probst@hey.com
License: MIT
Project-URL: Documentation, https://github.com/daenuprobst/drfp
Project-URL: Source, https://github.com/daenuprobst/drfp
Project-URL: Twitter, https://twitter.com/skepteis
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/x-rst; charset=UTF-8
Provides-Extra: testing
License-File: LICENSE.txt
License-File: AUTHORS.rst

====
drfp
====


An NLP-inspired chemical reaction fingerprint based on basic set arithmetic.


Description
===========

Predicting the nature and outcome of reactions using computational methods is an important tool to accelerate chemical research. The recent application of deep learning-based learned fingerprints to reaction classification and reaction yield prediction has shown an impressive increase in performance compared to previous methods such as DFT- and structure-based fingerprints. However, learned fingerprints require large training data sets, are inherently biased, and are based on complex deep learning architectures. Here we present the differential reaction fingerprint \textit{DRFP}. The \textit{DRFP} algorithm takes a reaction SMILES as an input and creates a binary fingerprint based on the symmetric difference of two sets containing the circular molecular n-grams generated from the molecules listed left and right from the reaction arrow, respectively, without the need for distinguishing between reactants and reagents. We show that \textit{DRFP} outperforms DFT-based fingerprints in reaction yield prediction and other structure-based fingerprints in reaction classification, and reaching the performance of state-of-the-art learned fingerprints in both tasks while being data-independent.


.. _pyscaffold-notes:

Note
====

This project has been set up using PyScaffold 4.0.2. For details and usage
information on PyScaffold see https://pyscaffold.org/.


