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

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).