Exploring the role of density functional theory in the design of gold nanoparticles for targeted drug delivery: a systematic review.

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 ~ 10⁶ to ~ 10³ CPU h for a 50 nm AuNP, positioning hybrid ML/DFT as a transformative approach for next-generation nanomedicine.

Methods: This systematic evaluation covers DFT approaches including gradient-corrected (PBE), hybrid (B3LYP), and meta-GGA (M06-L) functionals, using relativistic basis sets (e.g., LANL2DZ) for Au atoms and polarized sets (e.g., 6-31G(d)) for organic ligands. Solvent effects are modeled via implicit (SMD) or explicit approaches. Time-dependent DFT (TD-DFT) analyzes localized surface plasmon resonance and frontier molecular orbitals. Multiscale approaches integrate DFT with molecular dynamics (MD) and machine learning interatomic potentials (MLIPs) to model extended systems, enabling simulations of AuNP-protein interactions for systems up to 10⁵ atoms with ~ 0.2 eV accuracy.