Computational Drug Discovery
Methods and Applications
(Sprache: Englisch)
Provide readers with an overview of modern technologies, emphasizing AI for drug discovery.
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Provide readers with an overview of modern technologies, emphasizing AI for drug discovery.
Inhaltsverzeichnis zu „Computational Drug Discovery “
Preface Volume 1: PART I. MOLECULAR DYNAMICS AND RELATED METHODS IN DRUG DISCOVERY Binding Free Energy Calculations in Drug Discovery Gaussian Accelerated Molecular Dynamics in Drug Discovery MD Simulations for Drug-Target (Un)Binding Kinetics Solvation Thermodynamics and its Competitive Saturation as a Paradigm of Co-Solvent Methods PART II. QUANTUM MECHANICS APPLICATION FOR DRUG DISCOVERY QM/MM Approaches for Structure Based Drug Design: Techniques and Applications Recent Advances in Practical Quantum Mechanics and Mixed-QM/MM Driven X-Ray Crystallography and Cryo-Electron Microscopy (Cryo-EM) and their Impact on Structure-Based Drug Discovery Quantum-Mechanical Analyses of Interactions for Biochemical Applications PART III. ARTIFICIAL INTELLIGENCE IN PRE-CLINICAL DRUG DISCOVERY The Role of Computer Aided Drug Design in Drug Discovery - An Introduction AI-Based Protein Structure Predictions and their Implications in Drug Discovery Deep Learning for the Structure-Based Binding Free Energy Prediction of Small Molecule Ligands Using Artificial Intelligence for the De Novo Drug Design and Retrosynthesis Reliability and Applicability Assessment for Machine Learning Models Volume 2: PART IV. CHEMICAL SPACE AND KNOWLEDGE BASED DRUG DISCOVERY Enumerable Libraries and Accessible Chemical Space Navigating Chemical Space Visualization, Exploration, and Screening of Chemical Space in Drug Discovery SAR Knowledge Based for Driving Drug Discovery Cambridge Structural Database (CSD) - Drug Discovery through Data Mining and Knowledge Based Tools PART V. STRUCTURE-BASED VIRTUAL SCREENING USING DOCKING Structure-Based Ultra-Large Scale Virtual Screenings Community Benchmarking Exercises for Docking and Scoring PART VI. IN SILICO ADMET MODELLING Advances in the Application of In Silico ADMET Models - An Industry Perspective PART VII. COMPUTATIONAL APPROACHES FOR NEW THERAPEUTIC MODALITIES Modelling the Structures of Ternary Complexes Mediated by Molecular Glues Free Energy
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Calculations in Covalent Drug Design PART VIII. COMPUTING TECHNOLOGIES DRIVING DRUG DISCOVERY Orion® A Cloud-Native Molecular Design Platform Cloud-Native Rendering Platform and GPUs Aid Drug Discovery The Quantum Computing Paradigm
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Autoren-Porträt
Vasanthanathan Poongavanam is a senior researcher in the Department of Chemistry-BMC, Uppsala University, Sweden. Before starting at Uppsala University in a researcher position with Jan Kihlberg in 2016, he was a postdoctoral fellow at the University of Vienna, Austria, and at the University of Southern Denmark. He obtained his Ph.D. in medicinal chemistry as a Drug Research Academy Fellow at the University of Copenhagen, Denmark, on computational modeling of Cytochrome P450. His research interests focus on understanding the molecular properties that govern the pharmacokinetic profile of molecules beyond the Ro5 space, including macrocycles and PROTACs. Vijayan Ramaswamy is a research scientist with the Structural Chemistry group at the Institute for Applied Cancer Science, University of Texas MD Anderson Cancer, TX, USA. Before starting at the MD Anderson Cancer in 2016, he was a postdoctoral fellow at Rutgers University, NJ, USA, and at Temple University, PA, USA. He received his Ph.D as a CSIR senior research fellow from the Indian Institute of Chemical Biology, Kolkata, India. His research focuses on applying computational chemistry methods to drive small-molecule drug discovery programs, particularly in oncology and neurodegenerative diseases.
Bibliographische Angaben
- 2024, 1. Auflage, 736 Seiten, 27 Schwarz-Weiß-Abbildungen, Maße: 17,6 x 25,1 cm, Gebunden, Englisch
- Herausgegeben: Vasanthanathan Poongavanam, Vijayan Ramaswamy
- Verlag: Wiley-VCH
- ISBN-10: 3527351663
- ISBN-13: 9783527351664
- Erscheinungsdatum: 29.02.2024
Sprache:
Englisch
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