Data-driven Design of PROTAC Linkers to Improve PROTAC Cell Membrane Permeability

Proteolysis-targeting chimeras (PROTACs) are promising next-generation therapeutics for the degradation of disease-associated proteins. However, optimizing the physicochemical properties of PROTACs, particularly their poor cell mem-brane permeability, remains challenging. Traditionally, PROTAC linkers have been manually designed to improve cell membrane permeability. Although recent machine learning-based approaches have enabled the rational design of PROTAC linkers, no linker design methods that explicitly address cell membrane permeability have been reported. In this study, we developed PROTAC-TS, a linker generative model that combines a chemical language model and reinforcement learning to control cell membrane permeability. We first constructed a prediction model of cell membrane permeability, which achieved high prediction performance (R2 = 0.710). By integrating this prediction model into the generative model, we successfully designed linkers of PROTACs with high predicted cell membrane permeability while considering PROTAC likeness. Our results highlight the potential of PROTAC-TS in accelerating PROTAC development with favorable cell membrane permea-bility.