Computational Methods in Medicinal Chemistry, Pharmacology, and Toxicology is a comprehensive resource that offers an advanced overview of computational techniques employed in drug discovery, design, and toxicity prediction. The book discusses various topics, including molecular modeling, virtual screening, machine learning, and network pharmacology. It serves as an essential guide for researchers, practitioners, and students in pharmacology, toxicology, medicinal chemistry, bioinformatics, and systems biology fields, showcasing practical applications and future perspectives on new technologies. In addition to covering computational approaches, the book provides real-world examples of drug discovery, candidate optimization, and safety assessment.
Other sections explore computer applications in pharmacology and toxicology and discusses the importance of these methods in advancing medicinal research.
1. Chapter 1 Introduction to computational methods in medicinal chemistry, pharmacology, and toxicology - p. 3 2. Chapter 2 Applications of machine learning for advanced drug discovery and design - p. 17 3. Chapter 3 Exploring deep-learning applications in drug discovery and design - p. 39 4. Chapter 4 Overview of pattern recognition, molecular descriptors, quantum mechanics, and representation methods - p. 59 5. Chapter 5 Exploring databases supporting computational pharmacology and toxicology techniques: An overview - p. 79 6. Part II Computer applications in medicinal chemistry, pharmacology and toxicology: Pharmaceutical, industrial, and clinical settings - p. 97 7. Chapter 6 QSAR and pharmacophore modeling in computational drug design - p. 99 8. Chapter 7 Docking in drug discovery: Principles, techniques, and applications - p. 119 9. Chapter 8 In silico molecular dynamics simulation - p. 133 10. Chapter 9 Computational techniques for enhancing PK/PD modeling and simulation and ADMET prediction - p. 153 11. Chapter 10 Predictive modeling in toxicology: Unveiling risks and ensuring safety - p. 175 12. Chapter 11 Integrated network analysis in pharmacology: Decoding interactions and pathways for therapeutic insights - p. 199 13. Part III Future perspectives on new technologies in medicinal chemistry, pharmacology and toxicology - p. 221 14. Chapter 12 An overview of computational tools and approaches for green molecular design to minimize toxicological risk in chemical compounds - p. 223 15. Chapter 13 Big data in computational medicinal chemistry, pharmacology and toxicology: Challenges and opportunities - p. 239 16. Chapter 14 Development of next-generation tools for advancing computational pharmacology and toxicology - p. 253 17. Chapter 15 Ethical considerations in the application of machine learning and artificial intelligence in medicinal chemistry, pharmacology, and toxicology - p. 275 18. Index - p. 285
Computational Methods in Medicinal Chemistry, Pharmacology, and Toxicology is a comprehensive resource that offers an advanced overview of computational techniques employed in drug discovery, design, and toxicity prediction. The book discusses various topics, including molecular modeling, virtual screening, machine learning, and network pharmacology. It serves as an essential guide for researchers, practitioners, and students in pharmacology, toxicology, medicinal chemistry, bioinformatics, and systems biology fields, showcasing practical applications and future perspectives on new technologies. In addition to covering computational approaches, the book provides real-world examples of drug discovery, candidate optimization, and safety assessment.
Other sections explore computer applications in pharmacology and toxicology and discusses the importance of these methods in advancing medicinal research.
1. Chapter 1 Introduction to computational methods in medicinal chemistry, pharmacology, and toxicology - p. 3 2. Chapter 2 Applications of machine learning for advanced drug discovery and design - p. 17 3. Chapter 3 Exploring deep-learning applications in drug discovery and design - p. 39 4. Chapter 4 Overview of pattern recognition, molecular descriptors, quantum mechanics, and representation methods - p. 59 5. Chapter 5 Exploring databases supporting computational pharmacology and toxicology techniques: An overview - p. 79 6. Part II Computer applications in medicinal chemistry, pharmacology and toxicology: Pharmaceutical, industrial, and clinical settings - p. 97 7. Chapter 6 QSAR and pharmacophore modeling in computational drug design - p. 99 8. Chapter 7 Docking in drug discovery: Principles, techniques, and applications - p. 119 9. Chapter 8 In silico molecular dynamics simulation - p. 133 10. Chapter 9 Computational techniques for enhancing PK/PD modeling and simulation and ADMET prediction - p. 153 11. Chapter 10 Predictive modeling in toxicology: Unveiling risks and ensuring safety - p. 175 12. Chapter 11 Integrated network analysis in pharmacology: Decoding interactions and pathways for therapeutic insights - p. 199 13. Part III Future perspectives on new technologies in medicinal chemistry, pharmacology and toxicology - p. 221 14. Chapter 12 An overview of computational tools and approaches for green molecular design to minimize toxicological risk in chemical compounds - p. 223 15. Chapter 13 Big data in computational medicinal chemistry, pharmacology and toxicology: Challenges and opportunities - p. 239 16. Chapter 14 Development of next-generation tools for advancing computational pharmacology and toxicology - p. 253 17. Chapter 15 Ethical considerations in the application of machine learning and artificial intelligence in medicinal chemistry, pharmacology, and toxicology - p. 275 18. Index - p. 285
