Integrating molecular generation and fingerprints transferring for single-molecule theranostics targeting endoplasmic reticulum stress.

INTRODUCTION

Precise diagnosis and treatment of diseases necessitate quantitative visualization and modulation of subcellular structures. The endoplasmic reticulum (ER), as one of the most essential organelles, presents a complex target due to its intricate morphology and diverse cellular roles. Regulating ER stress offers a promising strategy for treating diseases such as tumors. However, achieving accurate targeting and therapeutic intervention at the subcellular level remains a significant challenge. Thus, there is an urgent need for theranostic agents that can precisely target and modulate ER stress.

OBJECTIVES

This study proposes a novel AI-driven dual-targeting strategy combining "passive + active" mechanisms to efficiently design molecules that resolve the balance between passive ER enrichment and precise modulation. We aim to design multifunctional theranostic molecules that precisely target Grp78, a key biomarker of ER stress, at the atomic level, enabling concurrent imaging and modulation of ER stress.

METHODS

A machine learning (ML)-based molecular fingerprints transfer method was developed for passive targeting based on identified subcellular targeting substructures. Meanwhile, a deep learning (DL)-based 3D molecular generation model, PM-1, was designed for active targeting through specific receptor interactions. By transferring key fingerprints and fluorescent motifs into PM-1-generated molecules, desired theranostic agents were produced. Their key properties were validated via dynamic simulations and quantitative calculations, followed by wet experiments.

RESULTS

Guided by these strategies, we identified unreported ER-targeting rules by discovering key passive-targeting fingerprints derived from ML models, and generated diverse new structures with high affinity binding to Grp78. We successfully synthesized ABT-CN2, a multidimensional fluorescent agent that demonstrates cost-effective chemical structure (molecular weight <400), robust targeting capability (Pearson's correlation coefficient = 0.93), and potential antitumor activity (IC = 53.21 μM).

CONCLUSION

This work presents a new paradigm for the intelligent design of fluorescent molecular probes with precise organelle-targeting capabilities for integrated diagnosis and therapy.