aceLarge Language Model‐Assisted Prediction and Virtual Screening of Near‐Infrared‐II Fluorescent Molecules
ABSTRACT Fluorescence imaging in the second near‐infrared window (NIR‐II, 1000–1700 nm) has attracted increasing attention in biomedical applications owing to its deep tissue penetration, high spatial resolution, and low background autofluorescence. However, the rational optimization of high‐performance NIR‐II fluorophores remains challenging because of the complex relationship between molecular structure and optical properties. In this study, we integrated existing fluorescence datasets with NIR‐II fluorophore…
An LLM-based model predicted molecular emission wavelengths competitively, and its guided virtual screening identified two NIR-II fluorophore candidates that showed favorable imaging in vitro and in vivo.
The abstract describes the LLM's performance only as competitive with baseline models, and it reports a feasibility demonstration with just two experimentally tested candidates.
Evidence
- Peer-reviewedSmall2026-10-15
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Truvace Impact Record TRV-2026-1663, v1: “Large Language Model‐Assisted Prediction and Virtual Screening of Near‐Infrared‐II Fluorescent Molecules.” Truvace, 2026-10-17. /record/TRV-2026-1663 (accessed at citation time). sha256 0154baa202697af9…
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