# AI‐Driven Acceleration of Fluorescence Probe Discovery

## TL;DR

This paper introduces PROBY, an AI model that accelerates the discovery of fluorescent probes by predicting their optical properties and identifying target-specific candidates for imaging applications.

## Contribution

The novel contribution is the development of PROBY, an AI model trained on large datasets to identify and optimize fluorescent probes for specific targets.

## Key findings

- PROBY identified thousands of candidate molecules with target affinity and favorable optical properties from a library of 26,416 molecules.
- Three clinically relevant probes (PE859, obatoclax, and B3) were validated for applications in imaging and drug screening.
- Chemical modification of PE859 led to 859-2, which enabled in vivo two-photon imaging of tau pathology in mice.

## Abstract

Fluorescence imaging probes are indispensable tools for clinical navigation and preclinical research. However, the discovery of target‐specific probes is hampered by the scarcity of targetable fluorophore scaffolds, making the development process slow, costly, and heavily reliant on trial‐and‐error design. Here, we present a hybrid strategy that integrates AI with bioassays to accelerate the development of target‐specific fluorescent probes. We developed an AI model (PROBY) based on over one million molecule entries from nine datasets, capable of identifying fluorescent molecules and predicting seven key photophysical properties. Applying PROBY to a library of 26,416 target‐validated molecules, we identified thousands of candidates with both target affinity and favorable optical characteristics. Focusing on three clinically relevant targets (tau, BCL‐2, and TDP‐43), we validated AI‐identified candidates and discovered PE859, obatoclax, and B3, which supported applications in spectral analysis, drug screening, pathological labeling, cell imaging, and ex vivo tumor imaging. Guided by PROBY, we chemically modify PE859, yielding two optimized derivatives (859‐1 and 859‐2). With improved photophysical properties, 859‐2 enabled in vivo two‐photon imaging of tau pathology in transgenic mice. This hybrid AI‐bioassay strategy substantially broadens the accessible scaffold landscape for designing target‐specific fluorescence probes and provides a scalable, efficient, and cost‐effective framework for next‐generation probe discovery.

We present PROBY, an AI model trained on large‐scale datasets to predict key photophysical properties and accelerate the discovery of target‐specific fluorescent probes. By screening a target‐annotated library, PROBY identifies candidate probes for diverse targets and could guide probe optimization, enabling a range of in vitro and in vivo imaging applications.