Machine learning-enhanced nano-QSAR and multiscale modeling for predictive nanomedicine: applications in herbal therapeutics and neglected tropical diseases

Abstract

Nanomedicine has transformed targeted drug delivery, yet the complexity of nano-bio interactions continues to hinder rational nanoparticle design. Predictive computational approaches, particularly machine learning (ML), nano-quantitative structure-activity relationships (nano-QSAR), and multiscale modeling, emerge as essential tools for understanding and optimizing nanoparticle behavior across biological environments. This review synthesizes advances in nano-specific descriptors, classical and deep learning frameworks, physics-informed hybrid models, and simulations spanning quantum, atomistic, and tissue-level scales. We highlight two application domains where these strategies show distinct translational promise: herbal nanomedicine, where computational modeling supports the design of phytochemical-loaded nanoparticles with improved solubility, stability, and targeting, and neglected tropical diseases, where predictive tools enable optimization of macrophage targeting, parasite niche penetration, and performance under resource-limited conditions. Persistent challenges, including data scarcity, heterogeneity, limited reproducibility, and regulatory uncertainty, are critically examined. We propose future directions centered on standardized open datasets, foundation AI models, autonomous nanoparticle design, and patient-specific digital twin platforms. Collectively, these integrative computational strategies provide a roadmap for accelerating safe, effective, and context-adapted nanomedicine development. Integration of machine learning, nano-QSAR, and multiscale modeling enables predictive, rational design of nanomedicines for diverse disease applications.