UID:
almafu_9960074035402883
Format:
1 online resource (0 p.)
Edition:
1st ed.
ISBN:
9780081001059
,
0081001053
,
9780081000984
,
0081000987
Series Statement:
Woodhead Publishing series in biomedicine ; Number 81
Note:
Description based upon print version of record.
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Front Cover -- A Practical Guide to Rational Drug Design -- Copyright -- Dedication -- Contents -- Introduction to the Book -- Foreword -- Acknowledgements -- About the Author -- Part One: Structure-Based Ligand Design -- Chapter 1: Structures, Limitations, and Pitfalls -- 1.1 Introduction -- 1.2 The limitations of experimentally determined structures -- 1.3 The pitfalls of misusing structural information -- 1.3.1 Protein structural change upon ligand binding -- 1.4 Protein structural change upon activation -- References -- Chapter 2: Structure-Based Ligand Design I: With Structures of Protein/Lead Compound Complex Available -- 2.1 Introduction -- 2.2 Case study 1: BACE1 - Fill the pocket by growing a molecule -- 2.2.1 BACE1 as a promising Alzheimer target -- 2.2.2 Function of proteases and BACE1 -- 2.2.3 Reading and selecting the structures for modeling -- 2.2.4 Challenge -- 2.2.5 Protein structure preparation -- 2.2.6 Protonation of protein structures -- 2.2.7 Energy minimization -- 2.2.8 Extend the lead molecule -- 2.2.9 Guide the fragment searching with pharmacopheric features -- 2.2.10 Transform the fragment searching to linker searching -- 2.3 Case study 2: heat shock protein 90 - Restore the electrostatic complementarity -- 2.3.1 Hsp90 as a therapeutic target for cancer -- 2.3.2 Structure and function of Hsp90 -- 2.3.3 Ligand design toward steric and electrostatic complementarity -- 2.4 Case study 3: estrogen receptor α agonists recognized by optimized pharmacophore models -- 2.4.1 Nuclear receptors and estrogen receptor α -- 2.4.2 Structural basis of agonism and antagonism of estrogen receptor α -- 2.4.3 Challenge -- 2.4.4 Pharmacophore models and their optimization -- 2.4.5 Genetic algorithms for pharmacophore optimization -- 2.5 Summary -- References.
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Chapter 3: Structure-Based Ligand Design II: With Structure of Protein/Lead Compound Complex Unavailable -- 3.1 Introduction -- 3.2 Case study 1: Plk1 kinase domain inhibitors as antitumor drugs -- 3.2.1 Plk1 as an antitumor drug target -- 3.2.2 Structure of Plk1 -- 3.2.3 Challenge -- 3.2.4 Docking the lead compound to the Plk1 active site -- 3.3 Case study 2: XIAP inhibitors to trigger apoptosis as an antitumor therapy -- 3.3.1 Apoptosis and its regulation -- 3.3.2 Challenge -- 3.3.3 Structure of the XIAP-Smac complex -- 3.3.4 Structure-based ligand design of non-peptide XIAP inhibitors -- 3.4 Case study 3: Bcl-xl inhibitors as anticancer drugs -- 3.4.1 Interplay of Bcl-2 family members determines the fate of cells -- 3.4.2 Structures of the Bcl-xl-ABT-737 complex -- 3.4.3 Challenges -- 3.4.4 Scaffold hopping leading to novel Bcl-xl inhibitors -- 3.5 Case study 4: design of kinase/bromodomain-containing 4 dual inhibitors -- 3.5.1 Multitarget therapy -- 3.5.2 Bromodomains as drug targets -- 3.5.3 Common ground of kinase and bromodomain inhibitors -- 3.5.4 Pharmacophore queries to catch the key features in molecular recognition -- 3.5.5 Virtual screening for dual inhibitors of kinase and BRD4 -- References -- Chapter 4: Homology Modeling and Ligand-Based Molecule Design -- 4.1 Introduction -- 4.2 Case study 1: prediction of human Yes1 kinase structure -- 4.2.1 Yes1 as an antitumor drug target -- 4.2.2 Challenges -- 4.2.3 Homology modeling of Yes1 structure -- 4.2.3.1 Template selection: activation state matters -- 4.2.3.2 In silico protein engineering -- 4.2.4 Molecular docking to predict ligand binding modes -- 4.3 Case study 2: homology modeling of human melanocortin-4 receptor, a G protein-coupled receptor target -- 4.3.1 GPCRs as drug targets -- 4.3.2 Structures of GPCRs.
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4.3.3 Similarity analysis among GPCR structures -- 4.3.4 Conformational changes induced by activation -- 4.3.5 MC4R as a drug target -- 4.3.6 Challenges -- 4.3.7 Homology modeling of MC4R -- 4.3.8 Docking of a cyclic peptide into MC4R -- 4.4 Case study 3: ligand-based approaches to human MC4R -- 4.4.1 De-risk preclinical research with peptides -- 4.4.2 Challenge -- 4.4.3 Pharmacophore models derived from rigidified peptide agonist of MC4R -- 4.4.4 Optimizing pharmacophore queries using genetic algorithms -- 4.5 Summary -- 4.6 Summary of part I -- References -- Part Two: QSAR and ADMET Predictions -- Chapter 5 Quantitative Structure-Activity Relationships: Promise, Validations, and Pitfalls -- 5.1 Introduction -- 5.2 QSAR and its role in drug discovery -- 5.3 Preparation of datasets -- 5.4 Geometrical description of PLS and SVM -- 5.5 Support vector machine -- 5.6 Roles of molecular descriptors -- 5.7 Validation of QSAR models -- 5.8 Pitfalls in QSAR modeling -- 5.9 Summary -- References -- Chapter 6: Quantitative Structure-Property Relationships Models for Lipophilicity and Aqueous Solubility -- 6.1 Introduction -- 6.2 Lipophilicity as estimated by log P -- 6.3 Atom type-based molecular descriptors and their optimization -- 6.4 Less complex PLS linear regression model and highly accurate SVR model of log P -- 6.5 Is log D more relevant for drug discovery? -- 6.6 Aqueous solubility is a key property of drug molecules -- 6.7 QSPR modeling of aqueous solubility -- 6.8 Summary -- References -- Chapter 7: In Silico ADMET Profiling: Predictive Models for CYP450, P-gp, PAMPA, and hERG -- 7.1 Introduction -- 7.2 CYP450 for drug metabolism -- 7.3 Intestinal absorption and PAMPA models -- 7.4 Prediction of P-gp activities -- 7.5 Toxicity in drug discovery -- 7.6 Predictive models for hERG -- 7.7 Delivery of modeling results -- 7.8 Summary.
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References -- Index -- Back Cover.
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English
Language:
English
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