{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Probst D"],"funding":["Swiss National Science Foundation"],"pagination":["91-97"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8996827"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["1(2)"],"pubmed_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 <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 molecules"],"journal":["Digital discovery"],"pubmed_title":["Reaction classification and yield prediction using the differential reaction fingerprint DRFP."],"pmcid":["PMC8996827"],"funding_grant_id":["51NF40-185544"],"pubmed_authors":["Schwaller P","Probst D","Reymond JL"],"additional_accession":[]},"is_claimable":false,"name":"Reaction classification and yield prediction using the differential reaction fingerprint DRFP.","description":"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 molecules","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Apr","modification":"2025-04-18T12:11:12.718Z","creation":"2025-04-06T21:49:13.432Z"},"accession":"S-EPMC8996827","cross_references":{"pubmed":["35515081"],"doi":["10.1039/d1dd00006c"]}}