# Machine Learning Prediction of Analyte-Induced Fluorescence Perturbations in DNA-Functionalized Carbon Nanotubes

## TL;DR

This paper uses machine learning to predict how small molecules affect the fluorescence of DNA-functionalized carbon nanotubes, helping design better nanosensors.

## Contribution

The study introduces ML models to predict analyte-induced fluorescence changes in DNA–SWCNT nanosensors using chemical features and cross-validation.

## Key findings

- ML models achieved mean R² values of 0.2–0.4 and F1 scores of ∼0.8 for predicting fluorescence responses.
- Cross-validation outperformed ensemble methods in predicting responses for a blind set of molecules.
- ML captures structure–response patterns in small datasets, aiding nanosensor design.

## Abstract

Single-walled carbon nanotubes (SWCNTs) functionalized
with single-stranded
DNAs can function as near-infrared nanosensors for molecular analytes.
However, predicting which analytes elicit strong optical responses
for specific nanosensors remains challenging. We developed machine
learning (ML) models to predict analyte-induced fluorescence changes
in a DNA–SWCNT dopamine nanosensor. Using a data set of 63
small molecules sampling chemical space around dopamine, we encoded
analytes with RDKit fingerprints, with or without HOMO and LUMO energies,
and applied principal component analysis to identify structural motifs
associated with optical response strength. We trained support vector
regression and classification models using two strategies: ensembles
of 200 models and cross-validation. Regression models achieved mean R
2 values of 0.2–0.4, with cross-validation
outperforming ensembles, while classifiers reached mean F1 scores
of ∼0.8. Cross-validation performed best for predictions on
a blind set of 21 molecules. These findings show that ML can capture
structure–response patterns in modest data sets and guide in
silico DNA–SWCNT nanosensor design.