Recent Advances in Molecular Docking: Methods, Tools, and Applications offers an authoritative guide to modern molecular docking techniques, emphasizing the integration of artificial intelligence and machine learning. The volume covers foundational principles, computational workflows, and advanced scoring functions, providing researchers and practitioners with practical tools to improve drug-target interaction predictions. It highlights applications across pharmaceuticals, including cancer, neurological disorders, and infectious diseases, which are all supported by real-world case studies. Challenges in docking accuracy, reproducibility, validation, and ethical and regulatory considerations are also covered.
Authored by international experts, it bridges theoretical knowledge with practical applications, making it an essential resource for computational biologists, pharmacologists, and drug developers aiming to leverage AI-driven docking in precision medicine.
1. Introduction - p. xxxix 2. Section I. Fundamentals of molecular docking - p. S1 3. Chapter 1. Fundamentals of molecular docking: Concepts, terminology, docking types and scoring functions - p. 3 4. Chapter 2. Protein-ligand interaction and binding site prediction - p. 33 5. Chapter 3. Databases for molecular docking: PDB, ChEMBL, ZINC, PubChem - p. 51 6. Section II. Computational methods and workflows - p. S2 7. Chapter 4. Molecular docking workflows and pipelines - p. 75 8. Chapter 5. Molecular docking tools and their benchmarking: A practical guide for interdisciplinary researchers - p. 91 9. Chapter 6. Quantum mechanics and molecular mechanics (QM/MM) in molecular docking - p. 115 10. Chapter 7. Molecular dynamics and its integration with docking studies - p. 137 11. Section III. Advanced techniques and integrative approaches - p. S3 12. Chapter 8. Docking against GPCRs, ion channels, and membrane proteins - p. 169 13. Chapter 9. Ensemble and multitarget docking approaches for polypharmacology in drug discovery - p. 185 14. Chapter 10. Virtual screening and high-throughput docking: Finding inhibitors against P-gp transporter for treating hepatocellular carcinoma - p. 201 15. Section IV. Artificial intelligence and machine learning in docking - p. S4 16. Chapter 11. AI-driven molecular docking: Machine learning models, deep learning scoring, and advanced docking frameworks - p. 215 17. Chapter 12. TAMARIND: A unified framework for efficient and accurate ligand docking via deep learning - p. 231 18. Chapter 13. Unifying AI systems and elastic clouds for scalable CUDA-based molecular docking using task dependency trees - p. 249 19. Section V. Applications in drug discovery and therapeutics - p. S5 20. Chapter 14. Advances in structure-based drug design: Principles and case studies in therapeutic development - p. 267 21. Chapter 15. Docking in cancer, neurological disorders, and infectious diseases - p. 293 22. Chapter 16. Drug repurposing using docking approaches - p. 309 23. Chapter 17. In silico study of phytochemical, synthetic, and marine compounds for antitubercular (Mycobacterium tuberculosis) activity - p. 345 24. Chapter 18. Structure-based drug designing using plant-derived compounds: Case studies - p. 355 25. Chapter 19. Molecular docking meets multi-omics and systems biology: A new era of precision therapeutics - p. 387 26. Chapter 20. Leveraging digital twin technology with multi-omics and molecular docking for precision oncology - p. 413 27. Section VI. Validation, platforms, and future perspectives - p. S6 28. Chapter 21. Challenges in docking accuracy and reproducibility - p. 435 29. Chapter 22. Correlation of computational molecular docking predictions with experimental outcomes - p. 447 30. Chapter 23. Cloud and HPC platforms for molecular docking and simulation - p. 467 31. Chapter 24. Ethical, legal, and regulatory perspectives in computational drug design - p. 489 32. Index - p. 519
Recent Advances in Molecular Docking: Methods, Tools, and Applications offers an authoritative guide to modern molecular docking techniques, emphasizing the integration of artificial intelligence and machine learning. The volume covers foundational principles, computational workflows, and advanced scoring functions, providing researchers and practitioners with practical tools to improve drug-target interaction predictions. It highlights applications across pharmaceuticals, including cancer, neurological disorders, and infectious diseases, which are all supported by real-world case studies. Challenges in docking accuracy, reproducibility, validation, and ethical and regulatory considerations are also covered.
Authored by international experts, it bridges theoretical knowledge with practical applications, making it an essential resource for computational biologists, pharmacologists, and drug developers aiming to leverage AI-driven docking in precision medicine.
1. Introduction - p. xxxix 2. Section I. Fundamentals of molecular docking - p. S1 3. Chapter 1. Fundamentals of molecular docking: Concepts, terminology, docking types and scoring functions - p. 3 4. Chapter 2. Protein-ligand interaction and binding site prediction - p. 33 5. Chapter 3. Databases for molecular docking: PDB, ChEMBL, ZINC, PubChem - p. 51 6. Section II. Computational methods and workflows - p. S2 7. Chapter 4. Molecular docking workflows and pipelines - p. 75 8. Chapter 5. Molecular docking tools and their benchmarking: A practical guide for interdisciplinary researchers - p. 91 9. Chapter 6. Quantum mechanics and molecular mechanics (QM/MM) in molecular docking - p. 115 10. Chapter 7. Molecular dynamics and its integration with docking studies - p. 137 11. Section III. Advanced techniques and integrative approaches - p. S3 12. Chapter 8. Docking against GPCRs, ion channels, and membrane proteins - p. 169 13. Chapter 9. Ensemble and multitarget docking approaches for polypharmacology in drug discovery - p. 185 14. Chapter 10. Virtual screening and high-throughput docking: Finding inhibitors against P-gp transporter for treating hepatocellular carcinoma - p. 201 15. Section IV. Artificial intelligence and machine learning in docking - p. S4 16. Chapter 11. AI-driven molecular docking: Machine learning models, deep learning scoring, and advanced docking frameworks - p. 215 17. Chapter 12. TAMARIND: A unified framework for efficient and accurate ligand docking via deep learning - p. 231 18. Chapter 13. Unifying AI systems and elastic clouds for scalable CUDA-based molecular docking using task dependency trees - p. 249 19. Section V. Applications in drug discovery and therapeutics - p. S5 20. Chapter 14. Advances in structure-based drug design: Principles and case studies in therapeutic development - p. 267 21. Chapter 15. Docking in cancer, neurological disorders, and infectious diseases - p. 293 22. Chapter 16. Drug repurposing using docking approaches - p. 309 23. Chapter 17. In silico study of phytochemical, synthetic, and marine compounds for antitubercular (Mycobacterium tuberculosis) activity - p. 345 24. Chapter 18. Structure-based drug designing using plant-derived compounds: Case studies - p. 355 25. Chapter 19. Molecular docking meets multi-omics and systems biology: A new era of precision therapeutics - p. 387 26. Chapter 20. Leveraging digital twin technology with multi-omics and molecular docking for precision oncology - p. 413 27. Section VI. Validation, platforms, and future perspectives - p. S6 28. Chapter 21. Challenges in docking accuracy and reproducibility - p. 435 29. Chapter 22. Correlation of computational molecular docking predictions with experimental outcomes - p. 447 30. Chapter 23. Cloud and HPC platforms for molecular docking and simulation - p. 467 31. Chapter 24. Ethical, legal, and regulatory perspectives in computational drug design - p. 489 32. Index - p. 519
