A Computationally Assisted Approach for Designing Wearable Biosensors toward Non-invasive Personalized Molecular Analysis.

Wearable sweat sensors have the potential to revolutionize precision medicine as they can non-invasively collect molecular information closely associated with an individual's health status. However, the majority of clinically relevant biomarkers cannot be continuously detected in situ using existing wearable approaches. Molecularly imprinted polymers (MIPs) are a promising candidate to address this challenge but haven't yet gained widespread use due to their complex design and optimization process yielding variable selectivity. Here we introduce QuantumDock, an automated computational framework for universal MIP development toward wearable applications. QuantumDock utilizes density functional theory to probe molecular interactions between monomers and the target/interferent molecules to optimize selectivity, a fundamentally limiting factor for MIP development toward wearable sensing. A molecular docking approach is employed to explore a wide range of known and unknown monomers, and to identify the optimal monomer/crosslinker choice for subsequent MIP fabrication. Using an essential amino acid phenylalanine as the exemplar, we performed successful experimental validation of QuantumDock using solution-synthesized MIP nanoparticles coupled with ultraviolet-visible spectroscopy. Moreover, we designed a QuantumDock-optimized graphene-based wearable device that can perform autonomous sweat induction, sampling, and sensing. We, for the first time, demonstrate wearable non-invasive phenylalanine monitoring in human subjects toward personalized healthcare applications. This article is protected by copyright. All rights reserved.