# A Scoring Function for Monolayer-Protected Gold Nanoparticles Capable of Recognizing Small Organic Molecules in Solution

## TL;DR

This paper introduces a scoring function to predict how well gold nanoparticles with ligand coatings bind to small organic molecules, aiding in the design of nanosensors.

## Contribution

The novel contribution is a data-driven scoring function that estimates AuNP-analyte binding affinities with high accuracy.

## Key findings

- The scoring function achieved R² = 0.85 and MAE = 0.45 kcal/mol when validated against experimental data.
- Ligand flexibility, monolayer packing, and hydrogen bonding significantly influence binding interactions.
- The framework enables rapid in silico screening of AuNP-based nanosensors.

## Abstract

Ligand-coated gold nanoparticles (AuNPs) can act as self-organized
nanoreceptors capable of selectively recognizing small organic molecules
(analytes) in solution. This ability can be applied in several fields,
with NMR chemosensing being a notable example. To advance the rational
design of such AuNP-based nanosensors, we present a data-driven scoring
function to rapidly estimate AuNP–analyte binding affinities,
thus allowing fast in silico prescreening of ligand-coated
AuNP sensors. This scoring function implements chemical similarity,
hydrophobicity, and charge complementarity as key molecular descriptors,
demonstrating excellent predictive accuracy when validated against
experimental data (R
2 = 0.85, MAE = 0.45
kcal/mol). Enhanced sampling molecular dynamics on representative
systems revealed that ligand flexibility, monolayer packing, and hydrogen
bonding critically shape binding interactions, particularly for weak
binding systems. Together, these data-driven and atomistic insights
offer a robust framework for the rational design and optimization
of AuNP-based nanosensors.