Predicting Efficacy and Toxicity of Phytochemical Drugs with AI Models

Attention to phytochemicals as beneficial agents is increasing, and several efforts have been made to assess their worth and well being using several computational methods. Traditional approaches to drug development have contributed significantly to pharmacological advancement but often involve high investment, are cumbersome, and suffer from poor predictive accuracy. Recent advances in AI now permit data-driven prediction of the pharmacological properties of natural products more efficiently and precisely. This chapter describes the role of artificial intelligence models in predicting the efficacy-toxicity profile of phytochemical drugs by using models such as machine learning, deep learning, and neural networks. Additionally, it discusses the application of AI in identifying bio active compounds, performing in silico analysis of molecular interaction, and enhancing drug-likeness with minimal adverse properties. A strategy that incorporates systems biology, molecular docking, and multi-omics data for improving predictive accuracy is also discussed. In this chapter, the emerging role of explainable AI in model interpretability and transparency is explored. Despite such advances, challenges persist regarding dataset’s limitations, model vulnerability, and standardized computational frameworks. Overall, this chapter highlights how AI provides predictive, scalable, and personalized phytochemical drug development by offering a game-changing platform for safer and more efficient natural product-based therapeutics.