Full Description
This book explores the transformative convergence of AI and phytochemistry. The book shows how AI is revolutionizing the discovery, design, and development of plant-based bioactive compounds by providing tools for structure identification, pharmacological prediction, compound optimization, and synthesis planning. Through machine learning, data analytics, and computational modeling, it demonstrates how AI accelerates and advances traditional phytochemical research.
Phytochemistry—the study of natural plant compounds—has long been foundational to drug discovery. Historically reliant on labor-intensive experiments, the field now faces a paradigm shift. AI offers scalable, data-driven methods that boost speed and accuracy, bridging classical phytochemistry with modern pharmaceutical innovation.
The book begins with foundational chapters on traditional phytochemistry and AI integration, including data sourcing and supply chain challenges. It then covers AI-assisted techniques for synthesis, extraction, isolation, and structural elucidation of bioactives. Later sections focus on simulation and optimization of drug candidates—such as property prediction, docking, virtual screening, and optimization algorithms. The final chapters explore pharmacological applications, collaborative models, ethical and legal considerations, and AI's global impact in democratizing drug discovery.
This book is ideal for researchers, academicians, and professionals in pharmaceutical sciences, biotechnology, and phytochemistry. It also serves students, trainees, and policymakers interested in the intersection of AI and natural product-based drug discovery.
