Artificial Intelligence and Natural Photosensitizer-Based Nanopharmaceuticals in Photodynamic Therapy: Advanced Modeling, Data-Driven Optimization, and Translational Perspectives

Abstract

Photodynamic therapy (PDT) is a minimally invasive therapeutic modality based on the interaction between a photosensitizer (PS), light, and molecular oxygen to generate reactive oxygen species (ROS) capable of inducing localized cytotoxicity. Natural products provide a chemically diverse source of photosensitizers, including curcumin, hypericin, hypocrellin, chlorin derivatives, alkaloids, flavonoids, anthraquinones, and other photoactive scaffolds. However, their translational development remains limited by poor solubility, aggregation, instability, variable purity, limited tissue penetration, suboptimal pharmacokinetics, and insufficient formulation readiness. In parallel, artificial intelligence (AI), including machine learning (ML), deep learning (DL), quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) modeling, radiomics, and predictive analytics, is increasingly being applied to photosensitizer discovery, molecular property prediction, nanoformulation optimization, treatment planning, and precision PDT. This critical review evaluates the intersection between AI, natural photosensitizers, nanopharmaceutical development, and PDT, with emphasis on methodological strengths, current limitations, and translational priorities. A PRISMA 2020-inspired search strategy identified 27 studies for qualitative synthesis, comprising 11 review articles and 16 original investigations, while additional seminal references were used for historical and mechanistic contextualization. The analysis indicates that current AI applications in PDT are concentrated around molecular property prediction, QSAR/QSPR modeling, phototoxicity assessment, radiomics, image-guided therapy, and treatment-response prediction, whereas AI-guided exploration of natural photosensitizer chemical space and AI-assisted nanoformulation design remain comparatively underdeveloped. Key barriers include heterogeneous datasets, limited natural-product representation in predictive models, insufficient external validation, weak integration between formulation variables and photodynamic outcomes, and limited consideration of manufacturing and regulatory requirements. This review proposes an integrated AI-enabled translational framework connecting natural-product chemical diversity, photochemical prediction, nanocarrier optimization, precision PDT validation, and clinical implementation.