From Nature to Neural Networks: The Role of Artificial Intelligence in Selecting Plants for Cancer Drug Discovery

Abstract

Introduction: Cancer remains a global health burden, and while synthetic drugs offer effective treatment, issues like toxicity and resistance persist. Medicinal plants are rich in anticancer phytochemicals, but traditional drug discovery methods are slow and laborious. The integration of Artificial Intelligence (AI) is accelerating natural product research by enabling efficient screening, target prediction, and toxicity assessment.

Objectives: This review highlights the role of AI-including QSAR modeling, molecular docking, and predictive analytics—in discovering and optimizing plant-derived anticancer agents, with emphasis on its use in personalized therapy, multi-target drug design, and phytochemical repurposing.

Methods: Recent studies employing machine learning, deep learning, and computational modeling were reviewed to evaluate their application in phytochemical screening, pharmacophore mapping, and virtual simulation of molecular interactions.

Results: AI platforms enhanced the speed and accuracy of identifying potent phytochemicals, predicted toxicity and targets effectively, and supported the design of personalized cancer therapies. Case studies showed successful use of AI in optimizing plant-based anticancer compounds.

Conclusions: AI-driven strategies offer a powerful approach to phytochemical-based cancer therapy. The synergy between AI and medicinal plants promises faster, safer, and more personalized treatment options, emphasizing the need for cross-disciplinary collaboration.