Applications of Artificial Intelligence in Plant-Based Anticancer Drug Discovery and Development

Abstract

The discovery of novel anticancer therapeutics faces significant challenges, including extended development timelines and high failure rates. Plants serve as an important source for anticancer compounds like paclitaxel and vincristine, yet traditional phytochemical discovery methods remain inefficient. Artificial intelligence (AI) and machine learning (ML) present innovative solutions to expedite the identification, validation, and optimization of plant-derived anticancer agents. Advanced computational techniques, including virtual screening, molecular modeling, and network pharmacology, enable rapid evaluation of vast phytochemical spaces. These tools can predict bioactivities, simulate molecular interactions, and suggest structural modifications to enhance drug-like properties. Recent successes include the identification of novel flavonoids targeting specific kinases and the optimization of traditional medicine compounds for improved efficacy. Current challenges encompass limited dataset availability, chemical complexity of natural products, and the need for experimental validation. The integration of multi-omics data and the development of specialized AI architectures for natural product chemistry show promise in addressing these limitations. AI-guided bioprospecting, automated ethnomedicinal knowledge mining, and the design of synergistic phytochemical combinations represents a transformative approach in natural product drug discovery, potentially leading to more efficient development of plant-based cancer therapeutics