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.…
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