AI-Based Prediction of Phytochemical Bioactivity and Toxicity

Abstract

Phytochemicals represent a vast and chemically diverse source of therapeutic agents. However, experimental screening of their bioactivity and safety is timeconsuming, costly and often limited by availability of pure compounds. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to predict biological activity and toxicity of plant-derived molecules from chemical structure data. AI-based approaches such as quantitative structure– activity relationship (QSAR) modeling, deep learning, molecular docking integration, and network pharmacology enable rapid identification of bioactive phytochemicals and early toxicity assessment. These methods accelerate drug discovery from natural products while reducing experimental burden and ethical concerns of animal testing. This review discusses current AI techniques used for phytochemical bioactivity and toxicity prediction, available phytochemical databases, modeling strategies, and validation methods. Applications in anticancer, antimicrobial and anti-inflammatory phytochemical discovery are highlighted. Challenges such as data quality, chemical diversity, interpretability, and domain applicability are also addressed. Future perspectives include explainable AI, multi-omics integration and hybrid experimental–computational pipelines for safe and effective phytochemicalbased therapeutics.

KEYWORDS: phytochemicals, artificial intelligence, bioactivity prediction, toxicity prediction, QSAR, machine learning, natural products, drug discovery