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NIH grant 1R16EB039652-01: Machine Learning-Enhanced Molecular Modeling of Near-Infrared Fluorescent Materials for Bioimaging
Institute: {'code': 'EB', 'abbreviation': 'NIBIB', 'name': 'National Institute of Biomedical Imaging and Bioengineering', 'admin_org_id': '12500035', 'admin_funding_url': ''}. PI: Chun-I Wang. Award: $151168.
Start: 2026-09-05T00:00:00.
Project Summary/Abstract
Near-infrared (NIR) fluorescence imaging has become indispensable in modern medicine, enabling real-time
visualization for surgical guidance and deep-tissue diagnostics. Current FDA-approved probes are limited to the
NIR-I window (650–950 nm), where scattering and autofluorescence compromise performance. The second NIR
window (NIR-II, 1000–1700 nm) offers deeper penetration, reduced background, and improved signal-to-noise,
yet existing NIR-II probes, including polymer dots (Pdots), remain constrained by low quantum yields due to
aggregation-caused quenching (ACQ) and narrow energy band gaps. This gap limits clinical translation of
otherwise promising Pdots, which uniquely combine exceptional brightness, photostability, and biocompatibility.
This project addresses these barriers through machine learning (ML)–enhanced molecular modeling integrated
with multiscale simulations. The overarching objective is to establish predictive design rules for donor–acceptor
copolymers that yield bright, stable, and biocompatible NIR-II Pdots. Three complementary aims will be pursued:
(1) develop supervised ML models to efficiently predict electronic coupling, a key quantum descriptor of ACQ,
across millions of molecular dynamics–sampled configurations; (2) apply unsupervised ML methods, including
dimensionality reduction and feature-extraction networks, to identify backbone conformations that govern band
gaps and orbital distributions critical for NIR-II emission; and (3) curate high-level ab initio datasets of
optoelectronic descriptors for donor and acceptor building blocks, integrating them with experimental
measurements and data-driven optimization to discover promising copolymer frameworks. By uniting ML with
physics-based modeling, the project will overcome computational bottlenecks, replace trial-and-error synthesis
with predictive design, and establish transferable principles for NIR-II materials. Outcomes will include open
datasets, interpretable ML models, and systematic guidelines for suppressing ACQ and enhancing emission
efficiency. These advances will accelerate the development of next-generation fluorescent probes for image-
guided surgery, oncology, and cardiovascular interventions. Beyond scientific impact, the project will provide
interdisciplinary training at the interface of chemistry, materials science, and artificial intelligence, preparing
students for careers in AI-enabled biomedical research.document fetched 2026-09-10 22:51