Phytochemistry, Computational Tools and Databases in Drug Discovery presents the state-of-the-art in computational methods and techniques for drug discovery studies from medicinal plants. Various tools and databases for virtual screening and characterization of plant bioactive compounds and their subsequent predictions on biological targets for the discovery of new drugs against specific diseases are presented, along with computational tools for the prediction of the toxic effects of phytochemicals on living systems. The book also provides in-depth insight on the applications of these computational tools as well as the databases that describe the interactions of phytochemicals with diseases along with predictions for druggable bioactive compounds.
Useful for drug developers, medicinal chemists, toxicologists, phytochemists, plant biochemists and analytical chemists, this book clearly presents the various computational techniques, tools and databases for phytochemical research.
1. Phytochemistry, Computational Tools, and Databases in Drug Discovery - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. List of Illustrations - p. Page xi 8. List of Tables - p. Page xii 9. Chapter 1 : Phytochemistry, history, and progress in drug discovery - p. Page xii 10. Chapter 2 : Trends in modern drug discovery and development: A glance in the present millennium - p. Page 20 11. Chapter 3 : Computational phytochemistry, databases, and tools - p. Page 35 12. Chapter 4 : Computational approaches in drug discovery from phytochemicals - p. Page 51 13. Chapter 5 : Informatics and databases for phytochemical drug discovery - p. Page 79 14. Chapter 6 : In silico approaches in the repurposing of bioactive natural products for drug discovery - p. Page 121 15. Chapter 7 : Virtual screening of phytochemicals for drug discovery - p. Page 141 16. Chapter 8 : Roles of metagenomics and metabolomics in computational drug discovery - p. Page 174 17. Chapter 9 : Molecular docking and molecular dynamics in natural products-based drug discovery - p. Page 190 18. Chapter 10 : Computational screening of phytochemicals for anti-bacterial drug discovery - p. Page 210 19. Chapter 11 : Computational screening of phytochemicals for anti-viral drug discovery - p. Page 237 20. Chapter 12 : Computational screening of phytochemicals for anti-parasitic drug discovery - p. Page 253 21. Chapter 13 : Computational screening of phytochemicals for anti-diabetic drug discovery - p. Page 278 22. Chapter 14 : Computational screening of phytochemicals for anti-cancer drug discovery - p. Page 306 23. Chapter 15 : Application of artificial intelligence and machine learning in natural products-based drug discovery - p. Page 330 24. Chapter 16 : Roles of artificial intelligence and machine learning approach in natural products-based drug discovery - p. Page 351 25. Chapter 17 : Application of density functional theory (DFT) and response surface methodology (RSM) in drug discovery - p. Page 368 26. Chapter 18 : Therapeutic potentials of medicinal plants and significance of computational tools in anti-cancer drug discovery - p. Page 387 27. Index - p. Page 439
Phytochemistry, Computational Tools and Databases in Drug Discovery presents the state-of-the-art in computational methods and techniques for drug discovery studies from medicinal plants. Various tools and databases for virtual screening and characterization of plant bioactive compounds and their subsequent predictions on biological targets for the discovery of new drugs against specific diseases are presented, along with computational tools for the prediction of the toxic effects of phytochemicals on living systems. The book also provides in-depth insight on the applications of these computational tools as well as the databases that describe the interactions of phytochemicals with diseases along with predictions for druggable bioactive compounds.
Useful for drug developers, medicinal chemists, toxicologists, phytochemists, plant biochemists and analytical chemists, this book clearly presents the various computational techniques, tools and databases for phytochemical research.
1. Phytochemistry, Computational Tools, and Databases in Drug Discovery - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page v 7. List of Illustrations - p. Page xi 8. List of Tables - p. Page xii 9. Chapter 1 : Phytochemistry, history, and progress in drug discovery - p. Page xii 10. Chapter 2 : Trends in modern drug discovery and development: A glance in the present millennium - p. Page 20 11. Chapter 3 : Computational phytochemistry, databases, and tools - p. Page 35 12. Chapter 4 : Computational approaches in drug discovery from phytochemicals - p. Page 51 13. Chapter 5 : Informatics and databases for phytochemical drug discovery - p. Page 79 14. Chapter 6 : In silico approaches in the repurposing of bioactive natural products for drug discovery - p. Page 121 15. Chapter 7 : Virtual screening of phytochemicals for drug discovery - p. Page 141 16. Chapter 8 : Roles of metagenomics and metabolomics in computational drug discovery - p. Page 174 17. Chapter 9 : Molecular docking and molecular dynamics in natural products-based drug discovery - p. Page 190 18. Chapter 10 : Computational screening of phytochemicals for anti-bacterial drug discovery - p. Page 210 19. Chapter 11 : Computational screening of phytochemicals for anti-viral drug discovery - p. Page 237 20. Chapter 12 : Computational screening of phytochemicals for anti-parasitic drug discovery - p. Page 253 21. Chapter 13 : Computational screening of phytochemicals for anti-diabetic drug discovery - p. Page 278 22. Chapter 14 : Computational screening of phytochemicals for anti-cancer drug discovery - p. Page 306 23. Chapter 15 : Application of artificial intelligence and machine learning in natural products-based drug discovery - p. Page 330 24. Chapter 16 : Roles of artificial intelligence and machine learning approach in natural products-based drug discovery - p. Page 351 25. Chapter 17 : Application of density functional theory (DFT) and response surface methodology (RSM) in drug discovery - p. Page 368 26. Chapter 18 : Therapeutic potentials of medicinal plants and significance of computational tools in anti-cancer drug discovery - p. Page 387 27. Index - p. Page 439
