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.
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.
Table of Contents
Section I: Fundamentals of Molecular Docking1. Fundamentals of Molecular Docking: Concepts, Terminology, Docking Types, and Scoring Functions
2. Protein-Ligand Interaction and Binding Site Prediction
3. Databases for Molecular Docking: PDB, ChEMBL, ZINC, PubChem
Section II: Computational Methods and Workflows
4. Molecular Docking Workflows and Pipelines
5. Molecular Docking Tools and Their Benchmarking: A Practical Guide for Interdisciplinary Researchers
6. Quantum Mechanics and Molecular Mechanics (QM/MM) in Docking
7. Molecular Dynamics and Its Integration with Docking Studies
Section III: Advanced Techniques and Integrative Approaches
8. Docking Against GPCRs, Ion Channels, and Membrane Proteins
9. Ensemble and Multi-Target Docking Approaches for Polypharmacology in Drug Discovery
10. Virtual Screening and High-Throughput Docking: Finding Inhibitors Against P-gp Transporter for Treating Hepatocellular Carcinoma (HCC)
Section IV: Artificial Intelligence and Machine Learning in Docking
11. AI-Driven Molecular Docking: Machine Learning Models, Deep Learning Scoring, and Advanced Docking Frameworks
12. TAMARIND: A Unified Framework for Efficient and Accurate Ligand Docking via Deep Learning
13. Unifying AI Systems and Elastic Clouds for Scalable CUDA-Based Molecular Docking Using Task Dependency Trees
Section V: Applications in Drug Discovery and Therapeutics
14. Advances in Structure-Based Drug Design: Principles and Case Studies in Therapeutic Development
15. Docking in Cancer, Neurological Disorders, and Infectious Diseases
16. Drug Repurposing Using Docking Approaches
17. In Silico Study of Phytochemical, Synthetic, and Marine Compounds for Anti-Tubercular (Mycobacterium tuberculosis) Activity
18. Structure-Based Drug Designing Using Plant-Derived Compounds: Case Studies
19. Molecular Docking Meets Multi-Omics and Systems Biology: A New Era of Precision Therapeutics
20. Leveraging Digital Twin Technology with Multi-Omics and Molecular Docking for Precision Oncology
Section VI: Validation, Platforms, and Future Perspectives
21. Challenges in Docking Accuracy and Reproducibility
22. Correlation of Computational Molecular Docking Predictions with Experimental Outcomes
23. Cloud and HPC Platforms for Molecular Docking and Simulation
24. Ethical, Legal, and Regulatory Perspectives in Computational Drug Design
