AI-Assisted Development of Personalized Phytopharmacological Therapies

With increasing interest in phytochemicals as therapeutic agents, efforts are underway to evaluate their efficacy and safety using computational methods. People have been curing with plants for generations, but now for the first time ever, artificial intelligence is improving this process. Plant-based medicines have been around for centuries, but now, AI is allowing scientists to discover new, previously unknown plant natural therapeutics. AI helps us analyze and detect patterns in data that were previously thought to be impossible. Because of this, scientists can spend their time more efficiently on the most promising molecules. AI gives scientists the ability to document and quantify parameters in real time, providing rational and objective therapeutic modifications. AI is in every field of Medicine, including Diagnosis, Therapy Optimization, Patient Management, and the expedited release of New Medicine. In Diagnostics, AI integrates everything past medical history, relevant images, genomics, and more often achieving results with greater accuracy and detail than standard methods. Machine learning improves the management of health of populations, resulting in fewer hospital readmissions and lower overall costs. AI also analyzes genetic and metabolic data to identify higher-risk patients, forecast the course of diseases, and detect the subtle signs that even the most seasoned clinicians can overlook. In this sense, the field of natural medicines is right now undergoing a huge transformation. Since the discovery of sophisticated tools, including spectroscopy, Natural Medicine has been undergoing significant advancements. Personalized medicines employ the use of genomic sequencing in identifying genes that affect an individual’s response to medicines and his or her susceptibility to certain diseases. It is applicable to pharmacogenomics to provide tailored biologic agent dosages to patients. Biomarker analysis is used to determine appropriate targeted therapies as well as predict the progression of disease. Multi-omics analyses, such as proteomics and metabolomics, help provide additional biological insights. Digital health tools and AI process various types of data for comprehensive monitoring and precise treatment. This chapter highlights how AI enables predictive, scalable, and personalized phytochemical drug development.