Exploring the role of density functional theory in the design of gold nanoparticles for targeted drug delivery: a systematic review.
Journal:
Journal of molecular modeling
Published Date:
Jun 10, 2025
Abstract
CONTEXT: Targeted drug delivery systems leveraging gold nanoparticles (AuNPs) demand precise atomic-level design to overcome current limitations in drug-loading efficiency and controlled release. Unlike previous focused reviews, this systematic analysis compares density functional theory's (DFT) performance across multiple AuNP design challenges, including drug interactions, surface functionalization, and stimuli-responsive behaviors. DFT predicts binding energies with ~ 0.1 eV accuracy and elucidates electronic properties of AuNP-drug complexes, critical for optimizing drug delivery. For example, B3LYP-D3/LANL2DZ calculations predict a - 0.58 eV binding energy for thioabiraterone, ensuring stable chemisorption via sulfur-Au bonds, as validated by experimental binding assays. However, high computational costs restrict its application to large biomolecular systems. Emerging hybrid machine learning (ML)/DFT approaches address scalability while preserving quantum-mechanical accuracy, reducing computational costs from ~ 10 to ~ 10 CPU h for a 50 nm AuNP, positioning hybrid ML/DFT as a transformative approach for next-generation nanomedicine.
