Multi-scale approaches in drug discovery : from empirical knowledge to in silico experiments and back / edited by Alejandro Speck-Planche
- Published:
- Amsterdam, Netherlands : Elsevier, 2017.
- Physical Description:
- 1 online resource
- Additional Creators:
- Speck-Planche, Alejandro
Access Online
- Contents:
- 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., 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., 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., 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., and 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 ...
- Subject(s):
- ISBN:
- 9780081012420 (electronic bk.)
008101242X (electronic bk.)
9780081011294 (print)
0081011296 - Note:
- Includes index.
View MARC record | catkey: 20047836