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|a Multi-scale approaches in drug discovery :
|b from empirical knowledge to in silico experiments and back /
|c edited by Alejandro Speck-Planche.
|
| 264 |
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1 |
|a Amsterdam, Netherlands :
|b Elsevier,
|c 2017.
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| 300 |
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|a 1 online resource
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
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|a online resource
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|6 880-01
|a 3.2.1. Creation of the Data Set and Calculation of the Molecular Descriptors3.2.2. Creation of the mtk-QSBER Model; 3.3. RESULTS AND DISCUSSION; 3.3.1. mtk-QSBER Model; 3.3.2. Molecular Descriptors and Their Meanings From a Physicochemical Point of View; 3.3.3. Contribution of Fragments to Multiple Biological Effects; 3.3.4. In Silico Design and Screening of Potentially Efficient and Safe Anti-HIV Molecules; 3.4. CONCLUSIONS; ACKNOWLEDGMENTS; REFERENCES; 4 -- Alkaloids From the Family Menispermaceae: A New Source of Compounds Selective for [beta]-Adrenergic Receptors; 4.1. INTRODUCTION.
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| 505 |
8 |
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|a 1.5. DETERMINATION OF HEAT CAPACITY CHANGES [delta]CP1.6. THE ACCURACY AND RELEVANCE OF ISOTHERMAL TITRATION CALORIMETRY DATA; 1.7. PROTEIN-LIGAND COMPLEX FORMATION: WHAT CAN THERMODYNAMIC DATA TELL ABOUT A GOOD STARTING POINT FOR OPTIMIZATION; 1.8. OPTIMIZATION: GO FOR ENTHALPY OR ENTROPY; 1.9. WHAT DOES AN H-BOND OR A LIPOPHILIC CONTACT CONTRIBUTE; 1.10. PAIN IN THE NECK: H-BONDS AND LIPOPHILIC CONTACTS ARE MUTUALLY DEPENDENT; 1.11. HARDLY AVOIDABLE: ENTHALPY/ENTROPY COMPENSATION; 1.12. WATER AND ITS IMPACT ON THE THERMODYNAMIC SIGNATURE.
|
| 505 |
0 |
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|a Front Cover; Multi-Scale Approaches in Drug Discovery; Multi-Scale Approaches in Drug Discovery: From Synthetic Methodologies and Biological; Copyright; Contents; Contributors; 1 -- Profiling Drug Binding by Thermodynamics: Key to Understanding; 1.1. THERMODYNAMICS: A CRITERION TO PROFILE PROTEIN-LIGAND BINDING; 1.2. QUANTIFYING BINDING AFFINITY IN PROTEIN-LIGAND COMPLEX FORMATION; 1.3. METHOD OF CHOICE TO ACCESS THERMODYNAMIC DATA: ISOTHERMAL TITRATION CALORIMETRY; 1.4. ISOTHERMAL TITRATION CALORIMETRY VERSUS VAN'T HOFF DATA TO ACCESS THERMODYNAMIC PROPERTIES.
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| 505 |
8 |
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|a 1.13. IMPACT OF SURFACE WATER MOLECULES ON THE THERMODYNAMIC SIGNATURE OF PROTEIN-LIGAND COMPLEXES1.14. CONCLUSION; REFERENCES; 2 -- Machine Learning Approach to Predict Enzyme Subclasses; 2.1. INTRODUCTION; 2.2. MATERIAL AND METHODS; 2.2.1. Background for Enzyme Subclasses Prediction; 2.2.2. Computational Model; 2.2.2.1. Input Parameters; 2.2.2.2. Data Set; 2.2.2.3. Multitarget QSAR Statistical Method; 2.3. RESULTS; 2.4. DISCUSSION; 2.5. CONCLUSIONS; ACKNOWLEDGMENTS; REFERENCES; 3 -- Multitasking Model for Computer-Aided Design and ; 3.1. INTRODUCTION; 3.2. MATERIALS AND METHODS.
|
| 500 |
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|a Includes index.
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| 588 |
0 |
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|a Online resource; title from PDF title page (ScienceDirect, viewed March 3, 2017).
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| 504 |
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|a Includes bibliographical references and index.
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| 520 |
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|a Drug discovery is an expensive, time-consuming process and the modern drug discovery community is constantly challenged not only with discovering novel bioactive agents to combat resistance from known diseases and fight against new ones, but to do so in a way that is economically effective. Advances in both experimental and theoretical/computational methods envisage that the greatest challenges in drug discovery can be most successfully addressed by using a multi-scale approach, drawing on the specialties of a whole host of different disciplines. Multi-Scale Approaches to Drug Discovery furnishes chemists with the detail they need to identify drug leads with the highest potential before isolating and synthesizing them to produce effective drugs with greater swiftness than classical methods may allow. This significantly speeds up the search for more efficient therapeutic agents. After an introduction to multi-scale approaches outlining the need for and benefits of their use, the book goes on to explore a range of useful techniques and research areas, and their potential applications to this process. Profiling drug binding by thermodynamics, machine learning for predicting enzyme sub-classes, and multitasking models for computer-aided design and virtual compound screening are discussed, before the book goes on to review Alkaloid Menispermaceae leads, natural chemotherapeutic agents and methods for speeding up the design and virtual screening of therapeutic peptides. Flavonoids as multi-target compounds are then explored, before the book concludes with a review of Quasi-SMILES as a novel tool.
|
| 650 |
|
0 |
|a Drug development.
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| 650 |
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2 |
|a Drug Discovery
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| 650 |
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6 |
|a Médicaments
|x Développement.
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| 650 |
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7 |
|a MEDICAL
|x Pharmacology.
|2 bisacsh
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| 650 |
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|a Drug development
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| 655 |
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7 |
|a Electronic books.
|2 local
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| 700 |
1 |
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|a Speck-Planche, Alejandro,
|e editor.
|
| 710 |
2 |
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|a ScienceDirect (Online service)
|
| 758 |
|
|
|i has work:
|a Multi-scale approaches in drug discovery (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCYy9ymhycyXxJDcdpC3TgX
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
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|i Print version:
|t Multi-scale approaches in drug discovery.
|z 0081011296
|z 9780081011294
|w (OCoLC)960278186
|
| 856 |
4 |
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|u http://proxy.library.tamu.edu/login?url=https://www.sciencedirect.com/science/book/9780081011294
|z Connect to the full text of this electronic book
|t 0
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| 880 |
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|6 505-01/(S
|a 4.1.1. β-Adrenergic Receptors4.1.2. Family Menispermaceae; 4.2. METHODS; 4.2.1. Data Set; 4.2.2. VolSurf Descriptors; 4.2.3. Models; 4.2.4. Docking; 4.3. RESULTS AND DISCUSSION; 4.4. CONCLUSION; ACKNOWLEDGMENTS; REFERENCES; 5 -- Natural Chemotherapeutic Agents for Cancer; 5.1. INTRODUCTION; 5.2. PLANTS AS A SOURCE OF CHEMOTHERAPEUTIC AGENTS; 5.3. DIETARY SUPPLEMENTS IN CHEMOTHERAPY; 5.4. OTHER NATURAL SOURCES OF CHEMOTHERAPEUTIC AGENTS; 5.5. CONCLUSION; REFERENCES; 6 -- Speeding Up the Virtual Design and Screening of Therapeutic Peptides: Simultaneous Prediction of Anticancer Act ...
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| 955 |
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|a Elsevier ScienceDirect 2026-2027
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|a Texas A&M University
|b College Station
|c Electronic Resources
|s www_evans
|d Available Online
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|h Library of Congress classification
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