From Empirical Design To Autonomous Ecosystems: AI-Driven Advances, Challenges, And Future Directions In Precision Nanomedicine

Engineering notes

Key topics: self-driving, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) into nanomedicine and drug delivery is driving a fundamental paradigm shift from empirical, trial-and-error formulation discovery toward predictive, data-driven, and patient-tailored therapeutic engineering. This comprehensive review systematically examines the multi-faceted convergence of AI technologies across the entire drug delivery pipeline. We highlight how ML and DL architectures including graph neural networks, generative adversarial frameworks, and transformer-based models predict nanoparticle physicochemical properties, optimize encapsulation efficiency, rationalize stimuli-responsive release kinetics, and accelerate target cell engagement. In drug discovery and development, AI streamlines target identification, virtual screening, ADMET profiling, and Quantitative Structure Activity Relationship (QSAR) modeling. Applied to formulation and biomanufacturing, AI enhances Process Analytical Technology (PAT) and Quality by Design (QbD) principles to ensure scalable, reproducible nanocarrier production. We further evaluate the transformative clinical impact of AI-guided delivery systems across major pathophysiological frontiers, including precision oncology, neurodegenerative disorders, cardiovascular and metabolic diseases, infectious disease vaccines, and advanced gene-editing nucleic acid therapeutics (e.g., lipid nanoparticles and exosomes). Despite remarkable advancements, key translational hurdles persist, notably data heterogeneity, model interpretability ("black-box" limitations), algorithmic domain shift, data privacy constraints, and a paucity of prospective clinical trials. To bridge these gaps, we outline an emerging futuristic paradigm anchored by multi-scale Digital Twins, autonomous self-driving laboratories utilizing closed-loop Design Make Test Analyze (DMTA) cycles, multi-omics and microphysiological system integration (Organ-on-a-Chip), generative inverse material design, and AI-assisted adaptive clinical trials. Ultimately, unifying computational prediction, autonomous experimentation, and clinical feedback into an integrated, continuously learning ecosystem promises to overcome current. translational bottlenecks, establishing AI as an essential cornerstone of next-generation, personalized nanomedicine.

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