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.

Table of Contents:
1. Phytochemistry, history and progress in drug discovery 2. Trends in modern-day drug discovery and development: a glance in the present millennium 3. Computational phytochemistry, databases and tools 4. Computational approaches in drug discovery from phytochemicals 5. Informatics and database for phytochemical drug discovery 6. In silico approaches in repurposing of bioactive natural products for drug discovery 7. Virtual screening of phytochemicals for drug discovery 8. Roles of metagenomics and metabolomics in computational drug discovery 9. Molecular docking and molecular dynamics in natural products-based drug discovery 10. Computational screening of phytochemicals for antibacterial drug discovery 11. Computational screening of phytochemicals for antiviral drug discovery 12. Computational screening of phytochemicals for antiparasitic drug discovery 13. Computational screening of phytochemicals for antidiabetic drug discovery 14. Computational screening of phytochemicals for anticancer drug discovery 15. Application of artificial intelligence and machine learning in natural products-based drug discovery 16. Roles of AI and machine learning approach in natural products-based drug discovery 17. Application of density functional theory (DFT) and response surface methodology (RSM) in drug discovery 18. Therapeutic potentials of medicinal plants and significance of computational tools in anticancer drug discovery