Case Studies: AI in Preclinical and Clinical Phytopharmacology

Advances in computing power, machine learning, and big data analytics have made AI a transformative tool for healthcare, biotechnology, and natural product research. In health care, AI enhances clinical decision-making, trial design, patient stratification, and pharmaco-vigilance, enabling precision medicine, integrating multi-omics with patient-specific data. Clinical pharmacologists will be important in guiding the development, evaluation, and deployment of AI tools and ensuring regulatory requirements within frameworks such as the European Commission AI Act are complied with. AI and ML have been comfortably integrated into natural product research, enabling the rapid screening, prioritization, and discovery of bioactive compounds from plants, microbes, and marine sources. Predictive identification of therapeutic molecules, elucidation of metabolic pathways, and diagnosis of plant diseases have been enabled by techniques like deep learning, computer vision, and QSAR modeling. AI has been used to perform metabolite profiling of Withania somnifera, evaluate antioxidant flavonoids, and optimize flavonoids extraction from plants in order to enhance yield, reproducibility, and efficiency. AI-driven approaches also find their applications in CADD, high-throughput screening, and development of personalized phytomedicine. Various case studies outline how AI accelerates the processes of antibiotic discovery, such as Halicin; optimizes the formulation of Traditional Chinese Medicine; enhances trial efficiency; and finally offers real-time monitoring of the efficacy of herbal drugs. AI in nanomedicine optimizes nanoparticle-based drug delivery processes and enhances targeting specificity, bioavailability, and controlled release profiles. Challenges for AI in natural product research relate not only to data quality but also to model explainability, integration across disciplines, and compliance with regulatory frameworks. AI holds great potential for further streamlining and facilitating more efficient drug discovery to enhance therapeutic efficacy and solve global health challenges through the identification of new compounds quickly, affordably, and with unprecedented precision. Integration of AI into natural product R&D belongs within the paradigm shift toward data-driven, personalized, and efficient drug discovery. The current chapter presents a description of how AI enables personalized, efficient, and scalable phytomedicine development from bench to bedside.