ArticleJACS Au2026
Data-Driven Design of PROTAC Linkers to Improve PROTAC Cell Membrane Permeability.
Article in JACS Au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Delivering Degradation: Nanomedicine and Programmable Proximity Platforms for Targeted Protein Degradation.Pharmaceutics · 2026Review
- Proteolysis-targeting chimera (PROTAC) in cancer: design principles and applications on "undruggable" targets.Biomarker research · 2026Review
- Targeted Protein Degradation in Cancer: PROTACs, New Targets, and Clinical Mechanisms.Biomolecules · 2026Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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 permeability, which achieved high prediction performance (
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