{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["8(6)"],"submitter":["Poongavanam V"],"pubmed_abstract":["Approaches for predicting proteolysis targeting chimera (PROTAC) cell permeability are of major interest to reduce resource-demanding synthesis and testing of low-permeable PROTACs. We report a comprehensive investigation of the scope and limitations of machine learning-based binary classification models developed using 17 simple descriptors for large and structurally diverse sets of cereblon (CRBN) and von Hippel-Lindau (VHL) PROTACs. For the VHL PROTAC set, kappa nearest neighbor and random forest models performed best and predicted the permeability of a blinded test set with >80% accuracy (<i>k</i> ≥ 0.57). Models retrained by combining the original training and the blinded test set performed equally well for a second blinded VHL set. However, models for CRBN PROTACs were less successfu"],"journal":["ACS omega"],"pagination":["5901-5916"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9933238"],"repository":["biostudies-literature"],"pubmed_title":["Predictive Modeling of PROTAC Cell Permeability with Machine Learning."],"pmcid":["PMC9933238"],"pubmed_authors":["Poongavanam V","Kolling F","Kihlberg J","Lehmann L","Meibom D","Giese A","Goller AH"],"additional_accession":[]},"is_claimable":false,"name":"Predictive Modeling of PROTAC Cell Permeability with Machine Learning.","description":"Approaches for predicting proteolysis targeting chimera (PROTAC) cell permeability are of major interest to reduce resource-demanding synthesis and testing of low-permeable PROTACs. We report a comprehensive investigation of the scope and limitations of machine learning-based binary classification models developed using 17 simple descriptors for large and structurally diverse sets of cereblon (CRBN) and von Hippel-Lindau (VHL) PROTACs. For the VHL PROTAC set, kappa nearest neighbor and random forest models performed best and predicted the permeability of a blinded test set with >80% accuracy (<i>k</i> ≥ 0.57). Models retrained by combining the original training and the blinded test set performed equally well for a second blinded VHL set. However, models for CRBN PROTACs were less successfu","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2025-04-04T19:00:16.595Z","creation":"2025-04-04T19:00:16.595Z"},"accession":"S-EPMC9933238","cross_references":{"pubmed":["36816707"],"doi":["10.1021/acsomega.2c07717"]}}