Full Description
"Recent Computational Techniques in De Novo Drug Design" gives a thorough overview of modern computational methods used to discover new chemical compounds. The book looks at how fragment-based design, evolutionary algorithms, free-energy-guided optimization, and deep generative models have helped advance molecular discovery. It also discusses important challenges such as synthetic accessibility and ADME/Tox issues. The book explains how structural bioinformatics, cheminformatics, and machine learning work together to speed up hit generation and lead optimization in both academic and industry settings.
The chapters start by introducing the basics of de novo drug design and explain how it differs from virtual screening and QSAR methods. The book describes the shift from rule-based techniques to those driven by artificial intelligence that use a wide range of molecular data. The content is organized into sections on structure-based and ligand-based methods, MD and QM approaches, deep learning applications, and case studies from different therapeutic areas.
Contents
Chapter 1. Fundamentals of Generative AI in Drug Development.- Chapter 2. Computational Design of mRNA-based Cancer Immunotherapies.- Chapter 3. Protein and Peptide Design with Generative AI.- Chapter 4. Exploring Chemical Space with Generative Models.- Chapter 5. Prediction of Drug Likeness and Synthetic Accessibility Using AI: A Case Study on Potential Therapeutic Compounds from Plants for SARS-CoV-2 Treatment.- Chapter 6. Challenges in AI-Generated Drug Candidates.- Chapter 7. AI-Driven Network Pharmacology in Natural Product-Based Drug Design: A Systems Bioinformatics Perspective.- Chapter 8. Applications of Generative AI in Small Molecule Drug Discovery.- Chapter 9. Generative AI for Antimicrobial and Antiviral Drug Design.- Chapter 10. Integration of AI-Driven Drug Design in Industry and Academia.- Chapter 11. Trends of Open-Source Tools and Frameworks for AI Drug Design.- Chapter 12. Future Horizons: Collaboration of Advanced Pharmaceutical Labs and Computational Sciences.
