Artificial Intelligence in Phytochemistry
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.
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Persons
Dr. Yamin Bibi is Chairperson of the Department of Botany at Rawalpindi Women University, Pakistan. Her research focuses on plant-based natural products, particularly the isolation and characterization of bioactive compounds and their pharmacological evaluation. She has supervised numerous PhD and MPhil students, completed funded projects, and published widely in national and international journals. Recipient of multiple research awards, she brings her expertise in natural product chemistry and leadership as Corresponding Editor to highlight how artificial intelligence can accelerate discovery of plant-derived therapeutics.
Dr. Kulsoom Zahara is an emerging scholar in phytochemistry and pharmacognosy, she specializes in isolating and identifying bioactive compounds using advanced techniques such as HPLC-DAD and GC-MS. Her research explores anticancer and antimicrobial properties of diverse plants, with growing emphasis on in silico and AI-driven approaches to predict bioactivity and safety. She contributes expertise in plant-based drug discovery and food safety, underscoring the transformative role of AI in modern phytochemical research.
Dr. Adeeb Shehzad is Associate Professor at the Biodiversity Research Center, Dhofar University, Oman. A globally recognized biomedical scientist, his pioneering work spans molecular biology, nanomedicine, and translational therapeutics. With advanced degrees from Kyungpook National University, South Korea, and prior appointments in Pakistan and Saudi Arabia, he has published over 100 papers with an h-index of 34. His research on cell signaling, nanomaterials, and regenerative medicine bridges fundamental science with clinical innovation. Listed among the World's Top 2% Scientists, he continues to advance biomedical education and sustainable health innovation.
Dr. Muhammad Jawad Khan is Associate Professor at Prince Sattam Bin Abdul Aziz University, Saudi Arabia. He specializes in artificial intelligence, disease modeling, and biomedical diagnostics, with a focus on neurological disorders. Holding a PhD in Mechanical Engineering from Pusan National University, South Korea, he has advanced hybrid EEG-fNIRS systems for neuro-robotics and medical diagnostics. His interdisciplinary work spans AI-driven modeling, robotics, and prosthetic limb design. Recognized among the World's Top 2% Researchers (Stanford, 2022), he has secured major research funding and led award-winning projects such as the iMeche UAS Challenge 2022.
Dr. Marcello Iriti is Professor of Plant Biology and Pathology at the University of Milan, Italy. His research covers phytochemistry, pharmacology, nutraceuticals, and functional foods, with emphasis on plant secondary metabolites and the Mediterranean diet. Author of over 230 publications, he has advanced understanding of antioxidants, essential oils, and phytotherapeutics in preclinical and clinical contexts. He serves on editorial boards, is a member of the World Academy of Sciences, Asian Council of Science Editors, and Italian Pharmacological Society, and is a founding member of the Italian Society of Environmental Medicine. A dedicated mentor, he exemplifies integration of traditional phytochemistry with modern biomedical sciences.
Content
Part I. Phytochemistry and drug discovery.- Chapter 1. Foundation of Phytochemistry and AI-Driven Innovations in Drug Discovery.- Part II. AI Simulation Techniques in phytochemistry.- Chapter 2. AI-Powered Prediction of Molecular Properties of Phytochemicals.- Chapter 3. Simulation of Drug-Phytochemical Interactions Using Artificial Intelligence (AI).- Chapter 4. AI-Driven Optimization of Phytochemical Drug Candidates.- Part III. Pharmacological Applications of AI in phytopharmacology.- Chapter 5. AI Tools for Designing Phytopharmacological Agents.- Chapter 6. Predicting Efficacy And Toxicity Of Phytochemical Drugs With Ai Models.- Chapter 7. Case Studies: AI in preclinical and Clinical phytopharmacology.- Part IV. Integration of AI in phytopharmacological Research.- Chapter 8. AI Techniques in the Discovery of Novel Phytochemical Compounds.- Chapter 9. AI-Assisted Development of Personalized phytopharmacological Therapies.- Chapter 10. Collaborative Strategies For Pharma Companies Using Ai In Phytopharmacology.- Part V. Challenges of artificial intelligence in the phytochemistry and drug discovery.- Chapter 11. Ethical Concerns in Leveraging AI for Phytochemistry.- Chapter 12. Legal and Regulatory Implications of AI Utilization in Drug Development. Chapter 13. Managing Security Risks in AI Deployment.- Chapter 14. Strategic Solutions for Overcoming Challenges in AI Adoption.