biostudies-literatureUnknown8(6)Poongavanam VApproaches 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 successfuACS omega5901-5916https://www.ebi.ac.uk/biostudies/studies/S-EPMC9933238biostudies-literaturePredictive Modeling of PROTAC Cell Permeability with Machine Learning.PMC9933238Poongavanam VKolling FKihlberg JLehmann LMeibom DGiese AGoller AHfalsePredictive Modeling of PROTAC Cell Permeability with Machine Learning.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 successfu2023-01-01T00:00:00Z2023 Feb2025-04-04T19:00:16.595Z2025-04-04T19:00:16.595ZS-EPMC99332383681670710.1021/acsomega.2c07717