Reaction classification and yield prediction using the differential reaction fingerprint DRFP.

ABSTRACT: Predicting the nature and outcome of reactions using computational methods is a crucial 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 DRFP. The 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

SUBMITTER: Probst D 

PROVIDER: S-EPMC8996827 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature