Fluor-tools: an integrated platform for dye property prediction and structure optimization

Abstract

With the rapid advancement of fluorescent dye research, there is an urgent need for tools capable of accurately predicting dye optical properties while facilitating structural modification. However, the field currently lacks reliable and user-friendly tools for this purpose. To address this gap, we have developed Fluor-tools—an integrated platform for dye property prediction and structural optimization. The platform comprises two core modules: (1) Fluor-pred, a dye property prediction model that integrates domain-specific knowledge of fluorophores with a label distribution smoothing (LDS) reweighting strategy and an advanced residual lightweight attention (RLAT) architecture. This model achieves state-of-the-art performance in predicting four key photophysical properties of dyes. (2) Fluor-opt, a structural optimization module that employs a matched molecular pair analysis (MMPA) method enhanced with symmetry-aware and environment-adaptive modifications. This module derives 1579 structural transformation rules, enabling the directional optimization of non-NIR (non-near-infrared) dyes to NIR properties. In summary, Fluor-tools provides robust computational support for research in biomedical imaging and optical materials. The platform is freely accessible at https://lmmd.ecust.edu.cn/Fluor-tools/.