biostudies-literatureProbst DSwiss National Science Foundation91-97https://www.ebi.ac.uk/biostudies/studies/S-EPMC8996827biostudies-literatureUnknown1(2)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 <i>DRFP</i>. The <i>DRFP</i> 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 <i>n</i>-grams generated from the moleculesDigital discoveryReaction classification and yield prediction using the differential reaction fingerprint DRFP.PMC899682751NF40-185544Schwaller PProbst DReymond JLfalseReaction classification and yield prediction using the differential reaction fingerprint DRFP.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 <i>DRFP</i>. The <i>DRFP</i> 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 <i>n</i>-grams generated from the molecules2022-01-01T00:00:00Z2022 Apr2025-04-18T12:11:12.718Z2025-04-06T21:49:13.432ZS-EPMC89968273551508110.1039/d1dd00006c