Dynamic Click Chemistry PROTAC Assembly via Microfluidic Gradient Optimization and Machine Learning-Guided Linker Selection

**Abstract:** This paper introduces a novel microfluidic platform integrated with machine learning (ML) to dynamically optimize the self-assembly of "click chemistry" PROTACs (Proteolysis-Targeting Chimeras) in aqueous environments. By precisely controlling reagent gradients and utilizing a predictive ML model trained on linker interaction data, we achieve significantly enhanced PROTAC assembly efficiency and homogeneity compared to traditional batch methods. This system promises to accelerate PROTAC drug discovery and development, enabling rapid optimization of target selectivity and proteolytic activity. The proposed technology is immediately adaptable for high-throughput screening of PROTAC formulations with potential for commercialization within a 3-5 year timeframe.