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

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 <i>in silico</i> 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 (<i>R</i>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.