Description
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.- Discusses in silico remodeling of effective phytochemical compounds for discovering improved anticancer agents for substantial/significant cancer therapy- Covers potential tools of bioinformatics that are applied toward discovering new targets by drug repurposing and strategies to cure different types of cancers- Demonstrates the significance of computational and artificial intelligence approaches in anticancer drug discovery- Explores how these various advances can be integrated into a precision and personalized medicine approach that can eventually enhance patient care
Table of Contents
Contributors About the editors Preface1. Computational approaches for anticancer drug design Tha Luong, Grace Persis Burri, Yuvasri Golivi, Ganji Purnachandra Nagaraju, and Bassel F. El-Rayes1. Introduction 2. Current computational approaches for cancer drug designs 3. Applications of computational approaches in cancer drug designing 4. Challenges and future directions 5. Conclusion References2. Molecular modeling approach in cancer drug therapy Bhavini Singh, Rishabh Rege, and Ganji Purnachandra Nagaraju1. Introduction 2. Drug designing 3. Molecular modeling 4. Methods of molecular modeling 5. Applications of molecular modeling 6. Applications in multidrug-resistant proteins 7. Conclusion References3. Discovery of anticancer therapeutics: Computational chemistry and Artificial Intelligence-assisted approach Subrata Das, Anupam Das Talukdar, Deepa Nath, and Manabendra Dutta Choudhury1. Introduction 2. Drug repurposing 3. Computational chemistry in drug designing 4. Structure-based drug designing 5. ADME/Tox screening and drug-likeness prediction 6. Molecular docking 7. Quantitative structure-activity relationship modeling 8. Molecular dynamics simulation 9. Artificial Intelligence in drug discovery 10. Conclusion References4. Artificial intelligence in oncological therapies Shloka Adluru1. Introduction 2. Importance of early diagnosis 3. How AI can improve accuracy and speed of cancer diagnoses 4. How AI can assess patient background information to determine risk of cancer 5. Diagnosis of cancer subtype and stage 6. AI in cancer drug discovery and development 7. De novo drug design 8. AI in recommending drug combinations and repurposing drugs 9. AI in identifying drug-target interactions 10. Deep learning, black boxes, and hidden layers 11. Future of AI in oncology 12. Conclusion References5. Approach of artificial intelligence in colorectal cancer and in precision medicine Grace Persis Burri, Yuvasri Golivi, Tha Luong, Neha Merchant, and Ganji Purnachandra Nagaraju1. Introduction2. Applications of AI in CRC3. Robotic-assisted surgery 4. Precision medicine in CRC 5. Benefits 6. Limitations 7. Current challenges and prospects 8. Conclusion Conflict of interest Funding References6. Artificial intelligence in breast cancer: An opportunity for early diagnosis Rama Rao Malla and Vedavathi Katneni1. Machine learning 2. Breast cancer 3. Conclusion References7. Quantitative structure-activity relationship and its application to cancer therapy Bhavini Singh and Rishabh Rege1. Introduction 2. Function 3. Origin of QSAR 4. Advanced techniques of QSAR 5. Application in drug design 6. Application in cancer therapy 7. Concerns 8. Conclusion References8. Structure-based virtual screening strategy for the identification of novel Greatwall kinase inhibitors Anbumani VelmuruganIlavarasi, Tulsi, Saswati Sarita Mohanty, Katike Umamahesh, Amouda Venkatesan, and Dinakara Rao Ampasala1. Introduction 2. Computational methods 3. Results and discussion 4. Conclusion Acknowledgments Conflict of interest References9. Strategies for drug repurposing Aparna Vema and Arunasree M. Kalle1. Introduction 2. Computational drug repurposing 3. Experimental drug repurposing 4. Conclusions and perspectives Author contributions Financial disclosures Conflict of interest References10. Principles of computational drug designing and drug repurposing—An algorithmic approach Angshuman Bagchi1. Introduction 2. Overview of basic thermodynamic principles involved in computational algorithms 3. Fundamentals of computational algorithms 4. Searching the conformational space 5. Analysis of protein flexibility 6. Drug repurposing 7. Conclusion Acknowledgment References11. Drug discovery and repositioning for glioblastoma multiforme and low-grade astrocytic tumors Asmita Dasgupta, Sanjukta Ghosh, Kastro Kalidass, and Shabnam Farisha1. Introduction 2. Approved therapeutics for astrocytic tumors 3. Drug discovery approaches against astrocytic tumors 4.
