drfp 0.2.2

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

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.

Note

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