Data-Driven Design of PROTAC Linkers to Improve PROTAC Cell Membrane Permeability.

ABSTRACT: 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 membrane 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 perme

SUBMITTER: Murakami Y 

PROVIDER: S-EPMC12933330 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature