NIRFluor: A Deep Learning Platform for Rapid Screening of Small Molecule Near-Infrared Fluorophores with Desired Optical Properties

IF 6.7 1区 化学 Q1 CHEMISTRY, ANALYTICAL Analytical Chemistry Pub Date : 2025-01-17 DOI:10.1021/acs.analchem.4c01953
Xiaozhi Wang, Hailong Wu, Tong Wang, Yao Chen, Baoshuo Jia, Huan Fang, Xiaoyue Yin, Yanping Zhao, Ruqin Yu
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Abstract

Small molecule near-infrared (NIR) fluorophores play a critical role in disease diagnosis and early detection of various markers in living organisms. To accelerate their development and design, a deep learning platform, NIRFluor, was established to rapidly screen small molecule NIR fluorophores with the desired optical properties. The core component of NIRFluor is a state-of-the-art deep learning model trained on 5179 experimental big data. First, novel hybrid fingerprints including Morgan fingerprints, physicochemical properties, and solvent properties were proposed. Then, a powerful deep learning model, multitask fingerprint-enhanced graph convolutional network (MT-FinGCN), was designed, which combines fingerprint information and molecule graph structure information to achieve accurate prediction of six properties (absorption wavelength, emission wavelength, Stokes shift, extinction coefficient, photoluminescence quantum yield, and lifetime) of different small molecule NIR fluorophores in different solvents. Furthermore, the “black-box” of the GCN model was opened through interpretability studies. Finally, the well-trained models were placed on the web platform NIRFluor for free use (https://nirfluor.aicbsc.com).

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来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
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