Revolutionizing Drug Discovery: Cutting-Edge Computational Techniques, Volume 103 is an essential guide for professionals, researchers, and students in the pharmaceutical and biotech industries, providing an in-depth look at how computational methods transform drug development. Chapters in this new release include Innovative Computational Approaches in Drug Discovery and Design, Advanced Molecular Modeling of Proteins: Methods, Breakthroughs, and Future Prospects, Predictive Cavity and Binding Site Identification: Techniques and Applications, ADMET Tools in the Digital Era: Applications and Limitations, Essential Database Resources for Modern Drug Discovery, Deep Learning for Drug Design and Development, and much more.
Other sections cover Molecular Docking and Structure-Based Drug Design: From Theory to Practice, Molecular Dynamics Simulations: Insights into Protein and Protein-Ligand Interactions, Targeting Disease: Computational Approaches for Drug Target Identification, High-throughput computational Screening for Lead Discovery and Development, Harnessing Machine Learning for Rational Drug Design, Identifying Novel Drug Targets with Computational Precision, Computational Exploration of Viral Cell Membrane Structures for Identifying Novel Therapeutic Target, and many more interesting topics.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. Contributors - p. xxiii 6. Preface - p. xxix 7. Chapter One: Innovative computational approaches in drug discovery and design - p. 1 8. Chapter Two: Advanced molecular modeling of proteins: Methods, breakthroughs, and future prospects - p. 23 9. Chapter Three: Predictive cavity and binding site identification: Techniques and applications - p. 43 10. Chapter Four: ADMET tools in the digital era: Applications and limitations - p. 65 11. Chapter Five: Essential database resources for modern drug discovery - p. 81 12. Chapter Six: Deep learning: A game changer in drug design and development - p. 101 13. Chapter Seven: Molecular and structure-based drug design: From theory to practice - p. 121 14. Chapter Eight: Molecular dynamics simulations: Insights into protein and protein ligand interactions - p. 139 15. Chapter Nine: Targeting disease: Computational approaches for drug target identification - p. 163 16. Chapter Ten: High-throughput computational screening for lead discovery and development - p. 185 17. Chapter Eleven: Harnessing machine learning for rational drug design - p. 209 18. Chapter Twelve: Identifying novel drug targets with computational precision - p. 231 19. Chapter Thirteen: Computational exploration of viral cell membrane structures for identifying novel therapeutic target - p. 265 20. Chapter Fourteen: The translational impact of bioinformatics on traditional wet lab techniques - p. 287 21. Chapter Fifteen: Pharmacophore modeling in drug design - p. 313 22. Chapter Sixteen: Emerging horizons of AI in pharmaceutical research - p. 325 23. Chapter Seventeen: Integrative computational approaches in pharmaceuticals: Driving innovation in discovery and delivery - p. 349 24. Chapter Eighteen: Innovations in vaccine design: Computational tools and techniques - p. 375 25. Chapter Nineteen: Real-world application of molecular docking in drug discovery - p. 393 26. Chapter Twenty: Challenges and limitations of computer-aided drug design - p. 415 27. Chapter Twenty One: Future prospective of AI in drug discovery - p. 429
Revolutionizing Drug Discovery: Cutting-Edge Computational Techniques, Volume 103 is an essential guide for professionals, researchers, and students in the pharmaceutical and biotech industries, providing an in-depth look at how computational methods transform drug development. Chapters in this new release include Innovative Computational Approaches in Drug Discovery and Design, Advanced Molecular Modeling of Proteins: Methods, Breakthroughs, and Future Prospects, Predictive Cavity and Binding Site Identification: Techniques and Applications, ADMET Tools in the Digital Era: Applications and Limitations, Essential Database Resources for Modern Drug Discovery, Deep Learning for Drug Design and Development, and much more.
Other sections cover Molecular Docking and Structure-Based Drug Design: From Theory to Practice, Molecular Dynamics Simulations: Insights into Protein and Protein-Ligand Interactions, Targeting Disease: Computational Approaches for Drug Target Identification, High-throughput computational Screening for Lead Discovery and Development, Harnessing Machine Learning for Rational Drug Design, Identifying Novel Drug Targets with Computational Precision, Computational Exploration of Viral Cell Membrane Structures for Identifying Novel Therapeutic Target, and many more interesting topics.
1. Cover image - p. i 2. Title page - p. ii 3. Table of Contents - p. iii 4. Copyright - p. iv 5. Contributors - p. xxiii 6. Preface - p. xxix 7. Chapter One: Innovative computational approaches in drug discovery and design - p. 1 8. Chapter Two: Advanced molecular modeling of proteins: Methods, breakthroughs, and future prospects - p. 23 9. Chapter Three: Predictive cavity and binding site identification: Techniques and applications - p. 43 10. Chapter Four: ADMET tools in the digital era: Applications and limitations - p. 65 11. Chapter Five: Essential database resources for modern drug discovery - p. 81 12. Chapter Six: Deep learning: A game changer in drug design and development - p. 101 13. Chapter Seven: Molecular and structure-based drug design: From theory to practice - p. 121 14. Chapter Eight: Molecular dynamics simulations: Insights into protein and protein ligand interactions - p. 139 15. Chapter Nine: Targeting disease: Computational approaches for drug target identification - p. 163 16. Chapter Ten: High-throughput computational screening for lead discovery and development - p. 185 17. Chapter Eleven: Harnessing machine learning for rational drug design - p. 209 18. Chapter Twelve: Identifying novel drug targets with computational precision - p. 231 19. Chapter Thirteen: Computational exploration of viral cell membrane structures for identifying novel therapeutic target - p. 265 20. Chapter Fourteen: The translational impact of bioinformatics on traditional wet lab techniques - p. 287 21. Chapter Fifteen: Pharmacophore modeling in drug design - p. 313 22. Chapter Sixteen: Emerging horizons of AI in pharmaceutical research - p. 325 23. Chapter Seventeen: Integrative computational approaches in pharmaceuticals: Driving innovation in discovery and delivery - p. 349 24. Chapter Eighteen: Innovations in vaccine design: Computational tools and techniques - p. 375 25. Chapter Nineteen: Real-world application of molecular docking in drug discovery - p. 393 26. Chapter Twenty: Challenges and limitations of computer-aided drug design - p. 415 27. Chapter Twenty One: Future prospective of AI in drug discovery - p. 429
