Computational Drug Discovery and Design
Springer-Verlag New York Inc.
978-1-0716-3440-0 (ISBN)
Authoritative and cutting-edge, Computational Drug Discovery and Design, Second Edition aims to effectively utilize computational methodologies in discovery and design of novel drugs.
lt;p>1. Computer-Aided Drug Discovery and Design - Recent Advances and Future Prospects
Alan Talevi
2. Virtual Screening Process - A Guide in Modern Drug Designing
Umesh Panwar, Aarthy Murali, Mohammad Aqueel Khan, Chandrabose Selvaraj, and Sanjeev Kumar Singh
3. Molecular dynamics as a tool for virtual ligand screening
Grégory Menchon, Laurent Maveyraud, and Georges Czaplicki
4. Antiviral Drug Target Identification and Ligand Discovery
Hershna Patel and Dipankar Sengupta
5. GRAMM webserver for protein docking
Amar Singh, Matthew M. Copeland, Petras J. Kundrotas and Ilya A. Vakser
6. Protein-ligand blind docking using CB-Dock2
Yang Liu and Yang Cao
7. Applications of Molecular Dynamics Simulations in Drug Discovery
Sara AlRawashdah and Khaled H Barakat
8. Molecular dynamics simulation-based prediction of glycosaminoglycan interactions with drug molecules
Martyna Maszota-Zieleniak and Sergey A. Samsonov
9. Mining chemogenomic spaces for prediction of drug-target interactions
Abhigyan Nath and Radha Chaube
10. Expanding the landscape of amyloid sequences with CARs-DB: a database of polar amyloidogenic peptides from disordered proteins
Carlos Pintado-Grima, Oriol Bárcenas, and Salvador Ventura
11. Accelerating molecular dynamics simulations for drug discovery
Kushal Koirala, Keya Joshi, Victor Adediwura, Jinan Wang, Hung Do, and Yinglong Miao
12. Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach
Aarthy Murali, Umesh Panwar, and Sanjeev Kumar Singh
13. Recent Deep-Learning Applications to Structure-Based Drug Design
Jacob Verburgt, Anika Jain, and Daisuke Kihara
14. Techniques for Developing Reliable Machine Learning Classifiers Applied to Understanding and Predicting Protein:Protein Interaction Hot Spots
Jiaxing Chen, Leslie A. Kuhn, and Sebastian Raschka
15. AI driven enhancements in drug screening and optimisation
Adam Serghini, Stephanie Portelli, and David B. Ascher
16. Applications of big data and AI-driven technologies in CADD (computer-aided drug design)
Seongmin Seo and Jai Woo Lee
17. Artificial Intelligence in ADME Property Prediction
Vishal B. Siramshetty, Xin Xu, and Pranav Shah
18. Accelerating the discovery and design of antimicrobial peptides with artificial intelligence
Mariana d. C. Aguilera-Puga, Natalia L. Cancelarich, Mariela M. Marani, Cesar de la Fuente-Nunez, and Fabien Plisson
| Erscheinungsdatum | 10.09.2023 |
|---|---|
| Reihe/Serie | Methods in Molecular Biology |
| Zusatzinfo | 1 Illustrations, black and white; XI, 356 p. 1 illus. |
| Verlagsort | New York, NY |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Theorie / Studium |
| Informatik ► Weitere Themen ► Bioinformatik | |
| Medizin / Pharmazie ► Medizinische Fachgebiete ► Pharmakologie / Pharmakotherapie | |
| Medizin / Pharmazie ► Pharmazie | |
| Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie | |
| Naturwissenschaften ► Chemie ► Physikalische Chemie | |
| Schlagworte | Chemoinformatics • computer-aided drug design • Drug target identification • Lead discovery and optimization • machine learning • Pharmacokinetics |
| ISBN-10 | 1-0716-3440-2 / 1071634402 |
| ISBN-13 | 978-1-0716-3440-0 / 9781071634400 |
| Zustand | Neuware |
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