Phytochemistry, Computational Tools, and Databases in Drug Discovery

Chukwuebuka Egbuna,Mithun Rudrapal,Habibu Tijjani

• Éditeur: Elsevier
• Année: 2022
• ISBN:
  9780323907163
• Sujet: Ciencias físicas
• Sous-sujet: Química orgánica

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.

• Provides the various databases, methods and procedures for computational drug discovery in plants
• Includes insights into the predictors for properties of phytochemicals against different diseases
• Discusses the applications of computational tools and their databases

Table des matières

• Cover
• Title page
• Table of Contents
• Copyright
• Contributors
• Chapter 1: Phytochemistry, history, and progress in drug discovery
• Chapter 2: Trends in modern drug discovery and development: A glance in the present millennium
• Chapter 3: Computational phytochemistry, databases, and tools
• Chapter 4: Computational approaches in drug discovery from phytochemicals
• Chapter 5: Informatics and databases for phytochemical drug discovery
• Chapter 6: In silico approaches in the repurposing of bioactive natural products for drug discovery
• Chapter 7: Virtual screening of phytochemicals for drug discovery
• Chapter 8: Roles of metagenomics and metabolomics in computational drug discovery
• Chapter 9: Molecular docking and molecular dynamics in natural products-based drug discovery
• Chapter 10: Computational screening of phytochemicals for anti-bacterial drug discovery
• Chapter 11: Computational screening of phytochemicals for anti-viral drug discovery
• Chapter 12: Computational screening of phytochemicals for anti-parasitic drug discovery
• Chapter 13: Computational screening of phytochemicals for anti-diabetic drug discovery
• Chapter 14: Computational screening of phytochemicals for anti-cancer drug discovery
• Chapter 15: Application of artificial intelligence and machine learning in natural products-based drug discovery
• Chapter 16: Roles of artificial intelligence and machine learning approach in natural products-based drug discovery
• Chapter 17: Application of density functional theory (DFT) and response surface methodology (RSM) in drug discovery
• Chapter 18: Therapeutic potentials of medicinal plants and significance of computational tools in anti-cancer drug discovery
• Index