Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research as well as computational approaches for drug discovery and repurposing for cancer therapy. The book also provides detailed descriptions about target molecules, pathways, and their inhibitors for easy understanding and applicability.
The book discusses tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers, and transcriptome; those are discussed in the context of different cancer types, such as colon, pancreatic, glioblastoma, endometrial, and retinoblastoma, among others.
This book is a valuable resource for researchers, students, and members of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor the treatment for cancer patients.
1. Computational Methods in Drug Discovery and Repurposing for Cancer Therapy - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Dedication - p. Page vi 7. Contributors - p. Page vi 8. About the editors - p. Page x 9. Preface - p. Page xi 10. List of Illustrations - p. Page xiii 11. List of Tables - p. Page xiv 12. Chapter 1 : Computational approaches for anticancer drug design - p. Page xiv 13. Chapter 2 : Molecular modeling approach for cancer drug therapy - p. Page 9 14. Chapter 3 : Discovery of anticancer therapeutics: Computational chemistry and Artificial Intelligence-assisted approach - p. Page 17 15. Chapter 4 : Artificial intelligence in oncological therapies - p. Page 34 16. Chapter 5 : Approach of artificial intelligence in colorectal cancer and in precision medicine - p. Page 55 17. Chapter 6 : Artificial intelligence in breast cancer: An opportunity for early diagnosis - p. Page 68 18. Chapter 7 : Quantitative structure-activity relationship and its application to cancer therapy - p. Page 86 19. Chapter 8 : Structure-based virtual screening for the identification of novel Greatwall kinase inhibitors - p. Page 98 20. Chapter 9 : Strategies for drug repurposing - p. Page 114 21. Chapter 10 : Principles of computational drug designing and drug repurposing—An algorithmic approach - p. Page 126 22. Chapter 11 : Drug discovery and repositioning for glioblastoma multiforme and low-grade astrocytic tumors - p. Page 139 23. Chapter 12 : Repurposing of phytocompounds-derived novel bioactive compounds possessing promising anticancer and cancer therapeutic efficacy through molecular docking, MD simulation, and drug-likeness/ADMET studies - p. Page 191 24. Chapter 13 : Old drugs and new opportunities—Drug repurposing in colon cancer prevention - p. Page 213 25. Chapter 14 : Repurposing cardiac glycosides as the hallmark of immunogenic modulators in cancer therapy - p. Page 233 26. Chapter 15 : Systems biology tools for the identification of potential drug targets and biological markers effective for cancer therapeutics - p. Page 253 27. Chapter 16 : Role of human body fluid biomarkers in liver cancer: A systematic review - p. Page 285 28. Chapter 17 : Study on biomarkers in endometrial cancer using transcriptome data: A machine learning approach - p. Page 305 29. Chapter 18 : Drug targeting PIWI like protein-piRNA complex, a novel paradigm in the therapeutic framework of retinoblastoma - p. Page 324 30. Chapter 19 : Emerging role of biosimilars: Focus on Bevacizumab and hepatocellular carcinoma - p. Page 366 31. Chapter 20 : Integrated computational approaches to aid precision medicine for cancer therapy: Present scenario and future prospects - p. Page 395 32. Index - p. Page 417
Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research as well as computational approaches for drug discovery and repurposing for cancer therapy. The book also provides detailed descriptions about target molecules, pathways, and their inhibitors for easy understanding and applicability.
The book discusses tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers, and transcriptome; those are discussed in the context of different cancer types, such as colon, pancreatic, glioblastoma, endometrial, and retinoblastoma, among others.
This book is a valuable resource for researchers, students, and members of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor the treatment for cancer patients.
1. Computational Methods in Drug Discovery and Repurposing for Cancer Therapy - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Dedication - p. Page vi 7. Contributors - p. Page vi 8. About the editors - p. Page x 9. Preface - p. Page xi 10. List of Illustrations - p. Page xiii 11. List of Tables - p. Page xiv 12. Chapter 1 : Computational approaches for anticancer drug design - p. Page xiv 13. Chapter 2 : Molecular modeling approach for cancer drug therapy - p. Page 9 14. Chapter 3 : Discovery of anticancer therapeutics: Computational chemistry and Artificial Intelligence-assisted approach - p. Page 17 15. Chapter 4 : Artificial intelligence in oncological therapies - p. Page 34 16. Chapter 5 : Approach of artificial intelligence in colorectal cancer and in precision medicine - p. Page 55 17. Chapter 6 : Artificial intelligence in breast cancer: An opportunity for early diagnosis - p. Page 68 18. Chapter 7 : Quantitative structure-activity relationship and its application to cancer therapy - p. Page 86 19. Chapter 8 : Structure-based virtual screening for the identification of novel Greatwall kinase inhibitors - p. Page 98 20. Chapter 9 : Strategies for drug repurposing - p. Page 114 21. Chapter 10 : Principles of computational drug designing and drug repurposing—An algorithmic approach - p. Page 126 22. Chapter 11 : Drug discovery and repositioning for glioblastoma multiforme and low-grade astrocytic tumors - p. Page 139 23. Chapter 12 : Repurposing of phytocompounds-derived novel bioactive compounds possessing promising anticancer and cancer therapeutic efficacy through molecular docking, MD simulation, and drug-likeness/ADMET studies - p. Page 191 24. Chapter 13 : Old drugs and new opportunities—Drug repurposing in colon cancer prevention - p. Page 213 25. Chapter 14 : Repurposing cardiac glycosides as the hallmark of immunogenic modulators in cancer therapy - p. Page 233 26. Chapter 15 : Systems biology tools for the identification of potential drug targets and biological markers effective for cancer therapeutics - p. Page 253 27. Chapter 16 : Role of human body fluid biomarkers in liver cancer: A systematic review - p. Page 285 28. Chapter 17 : Study on biomarkers in endometrial cancer using transcriptome data: A machine learning approach - p. Page 305 29. Chapter 18 : Drug targeting PIWI like protein-piRNA complex, a novel paradigm in the therapeutic framework of retinoblastoma - p. Page 324 30. Chapter 19 : Emerging role of biosimilars: Focus on Bevacizumab and hepatocellular carcinoma - p. Page 366 31. Chapter 20 : Integrated computational approaches to aid precision medicine for cancer therapy: Present scenario and future prospects - p. Page 395 32. Index - p. Page 417
