Bioinformatics, AI, and Machine Learning in Microbial Drug Development provides a comprehensive framework for integrating diverse fields like microbiology, bioinformatics, artificial intelligence, and machine learning, allowing readers to navigate complex interdisciplinary challenges effectively. This title offers an in-depth exploration of how these technologies are seamlessly integrated into pharmaceutical microbiology. The book, divided into 4 parts and 19 chapters, provides cutting-edge insights, practical guidance, and case studies. It emphasizes the importance of staying current with technological advancements, understanding ethical and regulatory issues, and optimizing drug production, making it an invaluable resource for industry professionals.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xvii 7. About the editors - p. xxi 8. Acknowledgments - p. xxiii 9. Section I: Foundations of microbial drug factories - p. S1 10. Chapter 1. Isolation and cultivation of novel microorganisms for drug prospecting - p. 3 11. Chapter 2. Natural product discovery and characterization - p. 23 12. Chapter 3. Synthetic biology and genetic engineering in pharmaceutical bioprospecting - p. 47 13. Section II: Advanced techniques and technologies - p. S75 14. Chapter 4. Multiomics data integration for drug discovery - p. 77 15. Chapter 5. Microbial biotechnology in the era of big data and AI - p. 91 16. Chapter 6. Computational tools for mining microbial genomes and metabolites - p. 113 17. Chapter 7. Omics technologies in microbial drug factories - p. 137 18. Chapter 8. Application of artificial intelligence for drug discovery - p. 159 19. Section III: Drug development and process optimization - p. S199 20. Chapter 9. High-throughput screening in microbial drug development - p. 201 21. Chapter 10. Fermentation process optimization for pharmaceutical production - p. 213 22. Chapter 11. Leveraging artificial intelligence for drug development and drug discovery - p. 233 23. Chapter 12. Metabolic engineering for drug production and drug discovery - p. 263 24. Chapter 13. Scaling up microbial drug production: strategies to overcome key challenges - p. 283 25. Section IV: Management, ethics, and future trends - p. S323 26. Chapter 14. Regulatory and ethical considerations in microbial drug production - p. 325 27. Chapter 15. Intellectual property rights and patent in microbial drug discovery - p. 339 28. Chapter 16. Overcoming challenges in microbial drug factory scale-up - p. 355 29. Chapter 17. Sustainability and green practices in microbial drug factories - p. 393 30. Chapter 18. Epilogue: microbes, technology, and the future of drug discovery - p. 405 31. Index - p. 425
Bioinformatics, AI, and Machine Learning in Microbial Drug Development provides a comprehensive framework for integrating diverse fields like microbiology, bioinformatics, artificial intelligence, and machine learning, allowing readers to navigate complex interdisciplinary challenges effectively. This title offers an in-depth exploration of how these technologies are seamlessly integrated into pharmaceutical microbiology. The book, divided into 4 parts and 19 chapters, provides cutting-edge insights, practical guidance, and case studies. It emphasizes the importance of staying current with technological advancements, understanding ethical and regulatory issues, and optimizing drug production, making it an invaluable resource for industry professionals.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xvii 7. About the editors - p. xxi 8. Acknowledgments - p. xxiii 9. Section I: Foundations of microbial drug factories - p. S1 10. Chapter 1. Isolation and cultivation of novel microorganisms for drug prospecting - p. 3 11. Chapter 2. Natural product discovery and characterization - p. 23 12. Chapter 3. Synthetic biology and genetic engineering in pharmaceutical bioprospecting - p. 47 13. Section II: Advanced techniques and technologies - p. S75 14. Chapter 4. Multiomics data integration for drug discovery - p. 77 15. Chapter 5. Microbial biotechnology in the era of big data and AI - p. 91 16. Chapter 6. Computational tools for mining microbial genomes and metabolites - p. 113 17. Chapter 7. Omics technologies in microbial drug factories - p. 137 18. Chapter 8. Application of artificial intelligence for drug discovery - p. 159 19. Section III: Drug development and process optimization - p. S199 20. Chapter 9. High-throughput screening in microbial drug development - p. 201 21. Chapter 10. Fermentation process optimization for pharmaceutical production - p. 213 22. Chapter 11. Leveraging artificial intelligence for drug development and drug discovery - p. 233 23. Chapter 12. Metabolic engineering for drug production and drug discovery - p. 263 24. Chapter 13. Scaling up microbial drug production: strategies to overcome key challenges - p. 283 25. Section IV: Management, ethics, and future trends - p. S323 26. Chapter 14. Regulatory and ethical considerations in microbial drug production - p. 325 27. Chapter 15. Intellectual property rights and patent in microbial drug discovery - p. 339 28. Chapter 16. Overcoming challenges in microbial drug factory scale-up - p. 355 29. Chapter 17. Sustainability and green practices in microbial drug factories - p. 393 30. Chapter 18. Epilogue: microbes, technology, and the future of drug discovery - p. 405 31. Index - p. 425
