Wiley-VCH - Bücher | Januar 2005 | Chemoinformatics in Drug Discovery

	Bücher \| Januar 2005 \| Chemoinformatics in Drug Discovery \| Inhaltsverzeichnis

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	Contents


	A Personal Foreword	XV
	Preface	XVII
	List of Contributors	XIX
1	Introduction to Chemoinformatics in Drug Discovery – A Personal View Garland R. Marshall	1
1.1	Introduction	1
1.2	Historical Evolution	4
1.3	Known versus Unknown Targets	5
1.4	Graph Theory and Molecular Numerology	6
1.5	Pharmacophore	7
1.6	Active-Analog Approach	8
1.7	Active-Site Modeling	9
1.8	Validation of the Active-Analog Approach and Active-Site Modeling	10
1.9	PLS/CoMFA	11
1.10	Prediction of Affinity	12
1.11	Protein Structure Prediction	13
1.12	Structure-Based Drug Design	15
1.13	Real World Pharmaceutical Issues	15
1.14	Combinatorial Chemistry and High-throughput Screens	16
1.15	Diversity and Similarity	16
1.16	Prediction of ADME	17
1.17	Failures to Accurately Predict	17
1.18	Summary	18
	References	19
Part I	Virtual Screening	23
2	Chemoinformatics in Lead Discovery Tudor I. Oprea	25
2.1	Chemoinformatics in the Context of Pharmaceutical Research	25
2.2	Leads in the Drug Discovery Paradigm	27
2.3	Is There a Trend for High Activity Molecules?	29
2.4	The Concept of Leadlikeness	32
2.5	Conclusions	37
	References	38
3	Computational Chemistry, Molecular Complexity and Screening Set Design Michael M. Hann, Andrew R. Leach, and Darren V.S. Green	43
3.1	Introduction	43
3.2	Background Concepts: the Virtual, Tangible and Real Worlds of Compounds, the ‘‘Knowledge Plot’’ and Target Tractability	44
3.3	The Construction of High Throughput Screening Sets	45
3.4	Compound Filters	47
3.5	‘‘Leadlike’’ Screening Sets	48
3.6	Focused and Biased Set Design	54
3.7	Conclusion	55
	References	56
4	Algorithmic Engines in Virtual Screening Matthias Rarey, Christian Lemmen, and Hans Matter	59
4.1	Introduction	59
4.2	Software Tools for Virtual Screening	61
4.3	Physicochemical Models in Virtual Screening	62
4.3.1	Intermolecular Forces in Protein–Ligand Interactions	63
4.3.2	Scoring Functions for Protein–Ligand Recognition	66
4.3.3	Covering Conformational Space	67
4.3.4	Scoring Structural Alignments	68
4.4	Algorithmic Engines in Virtual Screening	69
4.4.1	Mathematical Concepts	69
4.4.2	Algorithmic Concepts	76
4.4.3	Descriptor Technology	81
4.4.4	Global Search Algorithms	85


4.5	Entering the Real World: Virtual Screening Applications	89
4.5.1	Practical Considerations on Virtual Screening	89
4.5.2	Successful Applications of Virtual Screening	91
4.6	Practical Virtual Screening: Some Final Remarks	99
	References	101
5	Strengths and Limitations of Pharmacophore-Based Virtual Screening Dragos Horvath, Boryeu Mao, Rafael Gozalbes, Fr´ed´erique Barbosa, and Sherry L. Rogalski	117
5.1	Introduction	117
5.2	The ‘‘Pharmacophore’’ Concept: Pharmacophore Features	117
5.3	Pharmacophore Models: Managing Pharmacophore-related Information	118
5.4	The Main Topic of This Paper	119
5.5	The Cox2 Data Set	119
5.6	Pharmacophore Fingerprints and Similarity Searches	120
5.7	Molecular Field Analysis (MFA)-Based Pharmacophore Information	123
5.8	QSAR Models	125
5.9	Hypothesis Models	125
5.10	The Minimalist Overlay-Independent QSAR Model	126
5.11	Minimalist and Consensus Overlay-Based QSAR Models	128
5.12	Diversity Analysis of the Cox2 Compound Set	131
5.13	Do Hypothesis Models Actually Tell Us More Than Similarity Models About the Structural Reasons of Activity?	131
5.14	Why Did Hypothesis Models Fail to Unveil the Key Cox2 Site–Ligand Interactions?	134
5.15	Conclusions	136
	References	137
Part II	Hit and Lead Discovery	141
6	Enhancing Hit Quality and Diversity Within Assay Throughput Constraints Iain McFadyen, Gary Walker, and Juan Alvarez	143
6.1	Introduction	143
6.1.1	What Makes a Good Lead Molecule?	144
6.1.2	Compound Collections – Suitability as Leads	144
6.1.3	Compound Collections – Diversity	145
6.1.4	Data Reliability	146
6.1.5	Selection Methods	149
6.1.6	Enhancing Quality and Diversity of Actives	153
6.2	Methods	154
6.2.1	Screening Library	155
6.2.2	Determination of Activity Threshold	156
6.2.3	Filtering	156
6.2.4	High-Throughput Screen Clustering Algorithm (HTSCA)	157
6.2.5	Diversity Analysis	160
6.2.6	Data Visualization	161
6.3	Results	162
6.3.1	Peptide Hydrolase	162
6.3.2	Protein Kinase	167
6.3.3	Protein–Protein Interaction	168
6.4	Discussion and Conclusion	169
	References	172
7	Molecular Diversity in Lead Discovery: From Quantity to Quality Cullen L. Cavallaro, Dora M. Schnur, and Andrew J. Tebben	175
7.1	Introduction	175
7.2	Large Libraries and Collections	176
7.2.1	Methods and Examples for Large Library Diversity Calculations	177
7.3	Medium-sized/Target-class Libraries and Collections	181
7.3.1	Computational Methods for Medium-and Target-class Libraries and Collections	183
7.4	Small Focused Libraries	189
7.4.1	Computational Methods for Small and Focused Libraries	190
7.5	Summary/Conclusion	191
	References	192
8	In Silico Lead Optimization Chris M.W. Ho	199
8.1	Introduction	199
8.2	The Rise of Computer-aided Drug Refinement	200
8.3	RACHEL Software Package	201
8.4	Extraction of Building Blocks from Corporate Databases	201
8.5	Intelligent Component Selection System	203
8.6	Development of a Component Specification Language	205
8.7	Filtration of Components Using Constraints	207
8.8	Template-driven Structure Generation	208
8.9	Scoring Functions – Methods to Estimate Ligand–Receptor Binding	209
8.10	Target Functions	212
8.11	Ligand Optimization Example	214
	References	219
Part III	Databases and Libraries	221
9	WOMBAT: World of Molecular Bioactivity Marius Olah, Maria Mracec, Liliana Ostopovici, Ramona Rad, Alina Bora, Nicoleta Hadaruga, Ionela Olah, Magdalena Banda, Zeno Simon, Mircea Mracec, and Tudor I. Oprea	223
9.1	Introduction – Brief History of the WOMBAT Project	223
9.2	WOMBAT 2004.1 Overview	224
9.3	WOMBAT Database Structure	227
9.4	WOMBAT Quality Control	228
9.5	Uncovering Errors from Literature	231
9.6	Data Mining with WOMBAT	234
9.7	Conclusions and Future Challenges	235
	References	237
10	Cabinet – Chemical and Biological Informatics Network Vera Povolna, Scott Dixon, and David Weininger	241
10.1	Introduction	241
10.1.1	Integration Efforts, WWW as Information Resource and Limitations	241
10.1.2	Goals	243
10.2	Merits of Federation Rather than Unification	243
10.2.1	The Merits of Unification	244
10.2.2	The Merits of Federation	244
10.2.3	Unifying Disparate Data Models is Difficult, Federating them is Easy	245
10.2.4	Language is a Natural Key	246
10.3	HTTP is Appropriate Communication Technology	248
10.3.1	HTTP is Specifically Designed for Collaborative Computing	248
10.3.2	HTTP is the Dominant Communication Protocol Today	248
10.3.3	HTML Provides a Universally Accessible GUI	249
10.3.4	MIME ‘‘ Text/Plain’’ and ‘‘Application/Octet-Stream’’ are Important Catch-alls	249
10.3.5	Other MIME Types are Useful	250
10.3.6	One Significant HTTP Work-around is Required	250
10.4	Implementation	251
10.4.1	Daylight HTTP Toolkit	251
10.4.2	Metaphorics’ Cabinet Library	252
10.5	Specific Examples of Federated Services	252
10.5.1	Empath – Metabolic Pathway Chart	253
10.5.2	Planet – Protein–ligand Association Network	254
10.5.3	EC Book – Enzyme Commission Codebook	254
10.5.4	WDI – World Drug Index	254
10.5.5	WOMBAT – World of Molecular Bioactivity	255
10.5.6	TCM (Traditional Chinese Medicines), DCM (Dictionary of Chinese Medicine), PARK (Photo ARKive) and zi4	255
10.5.7	Cabinet ‘‘Download’’ Service	256
10.5.8	Cabinet Usage Example	256
10.6	Deployment and Refinement	262
10.6.1	Local Deployment	264
10.6.2	Intranet Deployment	264
10.6.3	Internet Deployment	265
10.6.4	Online Deployment	266
10.7	Conclusions	266

	References	268
11	Structure Modification in Chemical Databases Peter W. Kenny and Jens Sadowski	271
11.1	Introduction	271
11.2	Permute	274
11.2.1	Protonation and Formal Charges	274
11.2.2	Tautomerism	275
11.2.3	Nitrogen Configurations	276
11.2.4	Duplicate Removal	276
11.2.5	Nested Loop	276
11.2.6	Application Statistics	277
11.2.7	Impact on Docking	277
11.3	Leatherface	279
11.3.1	Protonation and Formal Charges	279
11.3.2	Tautomerism	280
11.3.3	Ionization and Tautomer Model	281
11.3.4	Relationships between Structures	282
11.3.5	Substructural Searching and Analysis	283
11.4	Concluding Remarks	283
	References	284
12	Rational Design of GPCR-specific Combinational Libraries Based on the Concept of Privileged Substructures Nikolay P. Savchuk, Sergey E. Tkachenko, and Konstantin V. Balakin	287
12.1	Introduction – Combinatorial Chemistry and Rational Drug Design	287
12.2	Rational Selection of Building Blocks Based on Privileged Structural Motifs	288
12.2.1	Privileged Structures and Substructures in the Design of Pharmacologically Relevant Combinatorial Libraries	288
12.2.2	Analysis of Privileged Structural Motifs: Structure Dissection Rules	291
12.2.3	Knowledge Database	293
12.2.4	Target-specific Differences in Distribution of Molecular Fragments	295
12.2.5	Privileged versus Peripheral Retrosynthetic Fragments	296
12.2.6	Peripheral Retrosynthetic Fragments: How to Measure the Target-specific Differences?	297
12.2.7	Selection of Building Blocks	300
12.2.8	Product-based Approach: Limiting the Space of Virtual Libraries	305
12.2.9	Alternative Strategy: Property-based Approach	306
12.2.10	Kohonen Self-organizing Maps	307
12.3	Conclusions	309
	References	311
Part IV	Chemoinformatics Applications	315
13	A Practical Strategy for Directed Compound Acquisition Gerald M. Maggiora, Veerabahu Shanmugasundaram, Michael S. Lajiness, Tom N. Doman, and Martin W. Schultz	317
13.1	Introduction	317
13.2	A Historical Perspective	319
13.3	Practical Issues	320
13.4	Compound Acquisition Scheme	322
13.4.1	Preprocessing Compound Files	322
13.4.2	Initial Compound Selection and Diversity Assessment	325
13.4.3	Compound Reviews	327
13.4.4	Final Selection and Compound Purchase	328
13.5	Conclusions	328
13.6	Methodologies	329
13.6.1	Preprocessing Filters	329
13.6.2	Diverse Solutions (DVS)	330
13.6.3	Dfragall	330
13.6.4	Ring Analysis	331
	References	331
14	Efficient Strategies for Lead Optimization by Simultaneously Addressing Affinity, Selectivity and Pharmacokinetic Parameters Karl-Heinz Baringhaus and Hans Matter	333
14.1	Introduction	333
14.2	The Origin of Lead Structures	336
14.3	Optimization for Affinity and Selectivity	338
14.3.1	Lead Optimization as a Challenge in Drug Discovery	338
14.3.2	Use and Limitation of Structure-based Design Approaches	339
14.3.3	Integration of Ligand-and Structure-based Design Concepts	340
14.3.4	The Selectivity Challenge from the Ligands’ Perspective	342
14.3.5	Selectivity Approaches Considering Binding Site Topologies	344
14.4	Addressing Pharmacokinetic Problems	347
14.4.1	Prediction of Physicochemical Properties	347
14.4.2	Prediction of ADME Properties	348
14.4.3	Prediction of Toxicity	349
14.4.4	Physicochemical and ADMET Property-based Design	350
14.5	ADME/Antitarget Models for Lead Optimization	350
14.5.1	Global ADME Models for Intestinal Absorption and Protein Binding	350
14.5.2	Selected Examples to Address ADME/Toxicology Antitargets	354
14.6	Integrated Approach	357
14.6.1	Strategy and Risk Assessment	357
14.6.2	Integration	359
14.6.3	Literature and Aventis Examples on Aspects of Multidimensional Optimization	360
14.7	Conclusions	366
	References	367
15	Chemoinformatic Tools for Library Design and the Hit-to-Lead Process: A User’s Perspective Robert Alan Goodnow, Jr., Paul Gillespie, and Konrad Bleicher	381
15.1	The Need for Leads: The Sources of Leads and the Challenge to Find Them	381
15.2	Property Predictions	383
15.3	Prediction of Solubility	384
15.4	Druglikeness	390
15.4.1	Are There Differences between Drugs and Nondrugs?	390
15.4.2	Is the Problem TractablewithinaSingle Program?	391
15.4.3	Do We Have a Training Set that Will Allow Us to Address the Issue?	392
15.4.4	Approaches to the Prediction of Druglikeness	392
15.5	Frequent Hitters	394
15.6	Identification of a Lead Series	395
15.7	The Hit-to-lead Process	397
15.7.1	Prioritization of Hits	397
15.7.2	Identification of Analogs	402
15.7.3	Additional Assays	403
15.8	Leads from Libraries: General Principles, Practical Considerations	404
15.9	Druglikeness in Small-molecule Libraries	406
15.10	Data Reduction and Viewing for Virtual Library Design	407
15.11	Druglikeness	408
15.12	Complexity and Andrews’ Binding Energy	408
15.13	Solubility	411
15.14	Polar Surface Area	411
15.15	Number of Rotatable Bonds	412
15.16	hERG Channel Binding	413
15.17	Chemoinformatic Analysis of the Predicted Hansch Substituent Constants of the Diversity Reagents for Design of Vector Exploration Libraries	415
15.18	Targeting Libraries by Virtual Screening	416
15.19	Combinatorial Design Based on Biostructural Information	418
15.20	Ligand-based Combinatorial Design: The RADDAR Approach	419
15.21	Virtual Screening of Small-molecule Library with Peptide-derived Pharmacophores	421
15.22	Chemoinformatic Tools and Strategies to Visualize Active Libraries	423
15.23	Visualization of Library Designs during Hit-to-lead Efforts	423
15.24	Summary and Outlook for Chemoinformatically Driven Lead Generation	425
	References	426
16	Application of Predictive QSAR Models to Database Mining Alexander Tropsha	437
16.1	Introduction	437
16.2	Building Predictive QSAR Models: The Importance of Validation	438
16.3	Defining Model Applicability Domain	441
16.4	Validated QSAR Modeling as an Empirical Data-modeling Approach: Combinatorial QSAR	443
16.5	Validated QSAR Models as Virtual Screening Tools	445
16.6	Conclusions and Outlook	452
	References	453
17	Drug Discovery in Academia – A Case Study Donald J. Abraham	457
17.1	Introduction	457
17.2	Linking the University with Business and Drug Discovery	457
17.2.1	Start-up Companies	457
17.2.2	Licensing	458
17.3	Research Parks	459
17.4	Conflict of Interest Issues for Academicians	459
17.5	Drug Discovery in Academia	461
17.5.1	Clinical Trials in Academia	461
17.6	Case Study: The Discovery and Development of Allosteric Effectors of Hemoglobin	462
17.6.1	Geduld (Patience)	463
17.6.2	Glück (Luck)	463
17.6.3	Geschick (Skill)	464
17.6.4	Geld (Money)	471
	References	481
	Subject Index	485

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