Current Status on Phytochemicals Classification, Structure-Activity Relationship, Stereochemistry and AI-Driven Applications: A Systematic Review - Al Saiqali, Mohammed; Aziz, Syeda Sameena; Reddy, Atla Venkateshwar (Pharmacognosy Reviews)
Phytochemicals, plant-derived bioactive compounds, offer notable health benefits and therapeutic applications. This study extensively covers phytochemicals, their classification, sources, bioactivities, and stereochemistry, categorizing them into groups like flavonoids, alkaloids, saponins, tannins, terpenoids, and lignans. Structure-Activity Relationship (SAR) studies reveal the crucial roles of specific structural features and their biological activities. The nitrogen atoms in alkaloids conjugated double bond systems in carotenoids, diverse functional groups such as Hydroxyl, carbonyl, and epoxy groups in terpenoids, hydroxyl groups in phenolic acids and flavonoids, glycosidic bonds in saponins, and the molecular architecture of lignans. These structural elements enable interactions with various biological targets, resulting in diverse pharmacological properties, including antioxidant, anti-inflammatory, antimicrobial, anticancer, cardioprotective, and other pharmacological activities. The current review highlights the significance of stereoisomerism in their bioactivities. In case of alkaloids, the spatial arrangement of the nitrogen atom plays a significant role in binding to receptors and enzymes. Whereas in carotenoids, the configuration of the conjugated double-bond system affects their antioxidant properties. The arrangement of hydroxyl groups in phenolic acids and flavonoids determines their ability to interact with their targets and exhibit antioxidant properties. The study examines the use of AI and machine learning in phytochemical research, providing insights into molecular modelling. Finally, the review offers a complete understanding of phytochemicals, their therapeutic applications and drug discovery.
Keywords: Phytocheical classification; SAR; Stereochemistry; AI-Driven drug discovery; Therapeutic potential