Computational Methods in Drug Discovery and Repurposing for Cancer Therapy

Contributors About the editors Preface 1. Computational approaches for anticancer drug design Tha Luong, Grace Persis Burri, Yuvasri Golivi, Ganji Purnachandra Nagaraju, and Bassel F. El-Rayes 1. 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 References 2. Molecular modeling approach in cancer drug therapy Bhavini Singh, Rishabh Rege, and Ganji Purnachandra Nagaraju 1. 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 References 3. Discovery of anticancer therapeutics: Computational chemistry and Artificial Intelligence-assisted approach Subrata Das, Anupam Das Talukdar, Deepa Nath, and Manabendra Dutta Choudhury 1. 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 References 4. Artificial intelligence in oncological therapies Shloka Adluru 1. 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 References 5. Approach of artificial intelligence in colorectal cancer and in precision medicine Grace Persis Burri, Yuvasri Golivi, Tha Luong, Neha Merchant, and Ganji Purnachandra Nagaraju 1. 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 References 6. Artificial intelligence in breast cancer: An opportunity for early diagnosis Rama Rao Malla and Vedavathi Katneni 1. Machine learning 2. Breast cancer 3. Conclusion References 7. Quantitative structure-activity relationship and its application to cancer therapy Bhavini Singh and Rishabh Rege 1. 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 References 8. 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 Ampasala 1. Introduction 2. Computational methods 3. Results and discussion 4. Conclusion Acknowledgments Conflict of interest References 9. Strategies for drug repurposing Aparna Vema and Arunasree M. Kalle 1. Introduction 2. Computational drug repurposing 3. Experimental drug repurposing 4. Conclusions and perspectives Author contributions Financial disclosures Conflict of interest References 10. Principles of computational drug designing and drug repurposing-An algorithmic approach Angshuman Bagchi 1. 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 References 11. Drug discovery and repositioning for glioblastoma multiforme and low-grade astrocytic tumors Asmita Dasgupta, Sanjukta Ghosh, Kastro Kalidass, and Shabnam Farisha 1. Introduction 2. Approved therapeutics for astrocytic tumors 3. Drug discovery approaches against astrocytic tumors 4.