Data Mining in Drug Discovery:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Weinheim
Wiley-VCH
2014
|
| Schriftenreihe: | Methods and Principles in Medicinal Chemistry
57 |
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XXII, 323 S. 15 farb. Ill. 240 mm x 170 mm |
| ISBN: | 9783527329847 9783527655984 |
Internformat
MARC
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| 245 | 1 | 0 | |a Data Mining in Drug Discovery |c ed. by Rémy D. Hoffmann ... |
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| 490 | 1 | |a Methods and Principles in Medicinal Chemistry |v 57 | |
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Datensatz im Suchindex
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CONTENTS
LIST OF CONTRIBUTORS XIII
PREFACE XVII
A PERSONAL FOREWORD XIX
PART ONE DATA SOURCES 1
1 PROTEIN STRUCTURAL DATABASES IN DRUG DISCOVERY 3
ESTHER KELLENBERGER AND DIDIER
ROGNAN
1.1 THE PROTEIN DATA BANK: THE UNIQUE PUBLIC ARCHIVE
OF PROTEIN STRUCTURES 3
1.1.1 HISTORY AND BACKGROUND: A WEALTHY RESOURCE FOR STRUCTURE-BASED
COMPUTER-AIDED DRUG DESIGN 3
1.1.2 CONTENT, FORMAT, AND QUALITY OF DATA: PITFALLS AND CHALLENGES
WHEN USING PDB FILES 5
1.1.2.1 THE CONTENT 5
1.1.2.2 THE FORMAT 6
1.1.2.3 THE QUALITY AND UNIFORMITY OF DATA 6
1.2 PDB-RELATED DATABASES FOR EXPLORING LIGAND-PROTEIN
RECOGNITION 9
1.2.1 DATABASES IN PARALLEL TO THE PDB 9
1.2.2 COLLECTION OF BINDING AFFINITY DATA 11
1.2.3 FOCUS ON PROTEIN-LIGAND BINDING SITES 11
1.3 THE SC-PDB, A COLLECTION OF PHARMACOLOGICALLY RELEVANT
PROTEIN-LIGAND COMPLEXES 12
1.3.1 DATABASE SETUP AND CONTENT 13
1.3.2 APPLICATIONS TO DRUG DESIGN 16
1.3.2.1 PROTEIN-LIGAND DOCKING 16
1.3.2.2 BINDING SITE DETECTION AND COMPARISONS 17
1.3.2.3 PREDICTION OF PROTEIN HOT SPOTS 19
1.3.2.4 RELATIONSHIPS BETWEEN LIGANDS AND THEIR TARGETS 19
1.3.2.5 CHEMOGENOMIC SCREENING FOR PROTEIN-LIGAND FINGERPRINTS 20
1.4 CONCLUSIONS 20
REFERENCES 21
HTTP://D-NB.INFO/1033857696
VII
CONTENTS
2 PUBLIC DOMAIN DATABASES FOR MEDICINAL CHEMISTRY 25
GEORGE NICOLA, TIQING LIU, AND MICHAEL CILSON
2.1 INTRODUCTION 25
2.2 DATABASES OF SMALL MOLECULE BINDING AND BIOACTIVITY 26
2.2.1 BINDINGDB 27
2.2.1.1 HISTORY, FOCUS, AND CONTENT 27
2.2.1.2 BROWSING, QUERYING, AND DOWNLOADING CAPABILITIES 27
2.2.1.3 LINKING WITH OTHER DATABASES 29
2.2.1.4 SPECIAL TOOLS AND DATA SETS 30
2.2.2 CHEMBL 31
2.2.2.1 HISTORY, FOCUS, AND CONTENT 31
2.2.2.2 BROWSING, QUERYING, AND DOWNLOADING CAPABILITIES 31
2.2.2.3 LINKING WITH OTHER DATABASES 32
2.2.2.4 SPECIAL TOOLS AND DATA SETS 33
2.2.3 PUBCHEM 34
2.2.3.1 HISTORY, FOCUS, AND CONTENT 34
2.2.3.2 BROWSING, QUERYING, AND DOWNLOADING CAPABILITIES 35
2.2.3.3 LINKING WITH OTHER DATABASES 37
2.2.3.4 SPECIAL TOOLS AND DATA SETS 37
2.2.4 OTHER SMALL MOLECULE DATABASES OF INTEREST 38
2.3 TRENDS IN MEDICINAL CHEMISTRY DATA 39
2.4 DIRECTIONS 44
2.4.1 STRENGTHENING THE DATABASES 44
2.4.1.1 COORDINATION AMONG DATABASES 44
2.4.1.2 DATA QUALITY 44
2.4.1.3 LINKING JOURNALS AND DATABASES 45
2.4.2 NEXT-GENERATION CAPABILITIES 46
2.5 SUMMARY 47
REFERENCES 48
3 CHEMICAL ONTOLOGIES FOR STANDARDIZATION, KNOWLEDGE DISCOVERY,
AND DATA MINING 55
JANNA HASTINGS AND CHRISTOPH STEINBECK
3.1 INTRODUCTION 55
3.2 BACKGROUND 56
3.2.1 THE OBO FOUNDRY. ONTOLOGIES IN BIOLOGY AND MEDICINE 57
3.2.2 ONTOLOGY LANGUAGES AND LOGICAL EXPRESSIVITY 58
3.2.3 ONTOLOGY INTEROPERABILITY AND UPPER-LEVEL
ONTOLOGIES 60
3.3 CHEMICAL ONTOLOGIES 60
3.4 STANDARDIZATION 64
3.5 KNOWLEDGE DISCOVERY 65
3.6 DATA MINING 68
3.7 CONCLUSIONS 70
REFERENCES 71
4
4.1
4.2
4.2.1
4.2.2
4.3
4.3.1
4.3.2
4.3.3
4.4
4.4.1
4.4.2
4.4.3
4.4.4
4.4.4.1
4.4.5
4.5
4.5.1
4.5.2
4.5.3
4.6
4.7
4.8
PART TWO
5
5.1
5.1.1
5.1.2
5.2
5.2.1
5.2.1.1
5.2.1.2
5.2.1.3
5.2.2
5.2.2.1
5.2.2.2
5.2.2.3
5.2.2.4
5.2.3
CONTENTS | VII
BUILDING A CORPORATE CHEMICAL DATABASE TOWARD SYSTEMS BIOLOGY 75
ELYETTE MARTIN, AURELIEN MONGE, MANUEL C. PEITSCH, AND PAVEL POSPISIL
INTRODUCTION 75
SETTING THE SCENE 76
CONCEPT OF MOLECULE, SUBSTANCE, AND BATCH 77
CHALLENGE OF REGISTERING DIVERSE DATA 78
DEALING WITH CHEMICAL STRUCTURES 79
CHEMICAL CARTRIDGES 79
UNIQUENESS OF RECORDS 80
USE OF ENHANCED STEREOCHEMISTRY 81
INCREASED ACCURACY OF THE REGISTRATION OF DATA 82
ESTABLISHING DRAWING RULES FOR SCIENTISTS 82
STANDARDIZATION OF COMPOUND REPRESENTATION 84
THREE ROLES AND TWO STAGING AREAS 85
BATCH REASSIGNMENT 87
UNKNOWN COMPOUNDS MANAGEMENT 87
AUTOMATIC PROCESSES 87
IMPLEMENTATION OF THE PLATFORM 88
DATABASE 88
SOFTWARE 89
DATA MIGRATION AND TRANSFORMATION OF NAMES INTO STRUCTURES 89
LINKING CHEMICAL INFORMATION TO ANALYTICAL DATA 91
LINKING CHEMICALS TO BIOACTIVITY DATA 93
CONCLUSIONS 97
REFERENCES 97
ANALYSIS AND ENRICHMENT 99
DATA MINING OF PLANT METABOLIC PATHWAYS 101
JAMES N.D. BATTEY AND NIKOLAI V. IVANOV
INTRODUCTION 101
THE IMPORTANCE OF UNDERSTANDING PLANT METABOLIC PATHWAYS 101
PATHWAY MODELING AND ITS PREREQUISITES 102
PATHWAY REPRESENTATION 103
COMPOUNDS 105
THE IMPORTANCE OF HAVING UNIQUELY DEFINED MOLECULES 105
REPRESENTATION FORMATS 105
KEY CHEMICAL COMPOUND DATABASES 108
REACTIONS 109
DEFINITIONS OF REACTIONS 109
IMPORTANCE OF STOICHIOMETRY AND MASS BALANCE 109
ATOM TRACING 109
STORING ENZYME INFORMATION: EC NUMBERS AND THEIR LIMITATIONS 110
PATHWAYS 111
VIII I CONTENTS
5.2.3.1 HOW ARE PATHWAYS DEFINED?
ILL
5.2.3.2 TYPICAL SIZE AND DISTINCTION BETWEEN PATHWAYS AND
SUPERPATHWAYS 111
5.3 PATHWAY MANAGEMENT PLATFORMS 111
5.3.1 KYOTO ENCYCLOPEDIA OF GENES AND GENOMES (KEGG) 113
5.3.1.1 DATABASE STRUCTURE IN KEGG 113
5.3.1.2 NAVIGATION THROUGH KEGG 113
5.3.2 THE PATHWAY TOOLS PLATFORM 113
5.3.2.1 DATABASE MANAGEMENT IN PATHWAY TOOLS 114
5.3.2.2 CONTENT CREATION AND MANAGEMENT WITH PATHWAY TOOLS 114
5.3.2.3 PATHWAY TOOLS'VISUALIZATION CAPABILITY 115
5.4 OBTAINING PATHWAY INFORMATION 116
5.4.1 "READY-MADE" REFERENCE PATHWAY DATABASES AND
THEIR CONTENTS 116
5.4.1.1 KEGG 116
5.4.1.2 METACYC AND PLANTCYC 116
5.4.1.3 METACROP 118
5.4.2 INTEGRATING DATABASES AND ISSUES INVOLVED 118
5.4.2.1 COMPOUND AMBIGUITY 118
5.4.2.2 REACTION REDUNDANCY 118
5.4.2.3 FORMATS FOR EXCHANGING PATHWAY DATA 119
5.4.3 ADDING INFORMATION TO PATHWAY DATABASES 120
5.4.3.1 MANUAL CURATION 120
5.4.3.2 AUTOMATED METHODS FOR LITERATURE MINING 121
5.5 * CONSTRUCTING ORGANISM-SPECIFIC PATHWAY DATABASES 122
5.5.1 ENZYME IDENTIFICATION 123
5.5.1.1 REFERENCE ENZYME DATABASES 123
5.5.1.2 ENZYME FUNCTION PREDICTION USING PROTEIN SEQUENCE
INFORMATION 123
5.5.1.3 ENZYME FUNCTION INFERENCE USING 3D PROTEIN STRUCTURE
INFORMATION 125
5.5.2 PATHWAY PREDICTION FROM AVAILABLE ENZYME INFORMATION 126
5.5.2.1 PATHWAY "PAINTING" USING KEGG REFERENCE MAPS 126
5.5.2.2 PATHWAY RECONSTRUCTION WITH PATHWAY TOOLS 126
5.5.3 EXAMPLES OF PATHWAY RECONSTRUCTION 126
5.6 CONCLUSIONS 127
REFERENCES 127
6 THE ROLE OF DATA MINING IN THE IDENTIFICATION OF BIOACTIVE
COMPOUNDS VIA HIGH-THROUGHPUT SCREENING 131
KAMAL AZZAOUI, JOHN P. PRIESTLE, THIBAULT VARIN, ANSGAR SCHUFFENHAUER,
JEREMY L JENKINS, FLORIAN NIGSCH, ALLEN COMETT, MAXIM POPOV,
AND EDGARJACOBY
6.1 INTRODUCTION TO THE HTS PROCESS: THE ROLE OF DATA MINING 131
6.2 RELEVANT DATA ARCHITECTURES FOR THE ANALYSIS OF HTS DATA 133
6.2.1 CONDITIONS (PARAMETERS) FOR ANALYSIS OF HTS SCREENS 133
CONTENTS
IIX
6.2.1.1 PURITY 133
6.2.1.2 ASSAY CONDITIONS 134
6.2.1.3 PREVIOUS PERFORMANCE OF SAMPLES 135
6.2.2 DATA AGGREGATION SYSTEM 135
6.3 ANALYSIS OF HTS DATA 136
6.3.1 ANALYSIS OF FREQUENT HITTERS AND UNDESIRABLE COMPOUNDS
IN HIT LISTS 136
6.3.2 ANALYSIS OF CELL-BASED SCREENING DATA LEADING TO MODE
OF MECHANISM HYPOTHESES 141
6.4 IDENTIFICATION OF NEW COMPOUNDS VIA COMPOUND SET ENRICHMENT
AND DOCKING 144
6.4.1 IDENTIFICATION OF HIT SERIES AND SAR FROM PRIMARY SCREENING
DATA BY COMPOUND SET ENRICHMENT 144
6.4.2 MOLECULAR DOCKING 147
6.5 CONCLUSIONS 150
REFERENCES 151
7 THE VALUE OF INTERACTIVE VISUAL ANALYTICS IN DRUG DISCOVERY:
AN OVERVIEW 155
DAVID MOSENKIS AND CHRISTOF CAENZLER
7.1 CREATING INFORMATIVE VISUALIZATIONS 156
7.2 LEAD DISCOVERY AND OPTIMIZATION 157
7.2.1 COMMON VISUALIZATIONS 157
7.2.1.1 SAR TABLES 157
7.2.1.2 SCATTER PLOTS 159
7.2.1.3 HISTOGRAMS 162
7.2.2 ADVANCED VISUALIZATIONS 162
7.2.2.1 PROFILE CHARTS 162
7.2.2.2 DOSE-RESPONSE CURVES 164
7.2.2.3 HEAT MAPS 164
7.2.3 INTERACTIVE ANALYSIS 166
7.3 GENOMICS 168
7.3.1 COMMON VISUALIZATIONS 168
7.3.1.1 HIERARCHICAL CLUSTERED HEAT MAP 168
7.3.1.2 SCATTER PLOT IN LOG SCALE 170
7.3.1.3 HISTOGRAMS AND BOX PLOTS FOR QUALITY CONTROL 171
7.3.1.4 KARYOGRAM (CHROMOSOMAL MAP) 171
7.3.2 ADVANCED VISUALIZATIONS 173
7.3.2.1 METABOLIC PATHWAYS 173
7.3.2.2 GENE ONTOLOGY TREE MAPS 174
7.3.2.3 CLUSTERED ALL TO ALL "HEAT MAPS" (TRIANGULAR HEAT MAP) 176
7.3.3 APPLICATIONS 177
7.3.3.1 UNDERSTANDING DISEASES BY COMPARING HEALTHY WITH UNHEALTHY
TISSUE OR PATIENTS 177
7.3.3.2 MEASURE EFFECTS OF DRUG TREATMENT ON A CELLULAR LEVEL 177
REFERENCES 178
X I CONTENTS
1
8
USING CHEMOINFORMATICS TOOLS FROM R 179
CITIES MARCOU AND IGOR 1. BASKIN
8.1 INTRODUCTION 180
8.2 SYSTEM CALL 180
8.2.1 PREREQUISITE 181
8.2.2 THE COMMAND SYSTEM() 181
8.2.3
EXAMPLE, COMMAND EDITION, AND OUTPUTS 181
8.3
SHARED LIBRARY CALL 185
8.3.1
SHARED LIBRARY 185
8.3.2
NAME MANGLING AND CALLING CONVENTION 187
8.3.3
DYN.LOAD AND DYN.UNLOAD 188
8.3.4
.C AND .FORTRAN 189
8.3.5
EXAMPLE 190
8.3.6
COMPILATION 190
8.4 WRAPPING 191
8.4.1
WHY WRAPPING 191
8.4.2 USING R INTERNALS 194
8.4.3 HOW TO KEEP AN SEXP ALIVE 195
8.4.4 BINDING TO C/C++ LIBRARIES 200
8.5
JAVA ARCHIVES 200
8.5.1 THE PACKAGE RJAVA 200
8.5.2 THE PACKAGE RCDK 202
8.6
CONCLUSIONS 206
REFERENCES 206
PART THREE APPLICATIONS TO POLYPHARMACOLOGY 209
9 CONTENT DEVELOPMENT STRATEGIES FOR THE SUCCESSFUL
IMPLEMENTATION OF DATA MINING TECHNOLOGIES 211
JORDI QUINTANA, ANTONI VALENCIA, ANDJOSEP PROUSJR.
9.1 INTRODUCTION 211
9.2 KNOWLEDGE CHALLENGES IN DRUG DISCOVERY 212
9.3 CASE STUDIES 213
9.3.1 THOMSON REUTERS INTEGRITY 213
9.3.1.1 KNOWLEDGE AREAS 215
9.3.1.2 SEARCH FIELDS 225
9.3.1.3 DATA MANAGEMENT FEATURES 227
9.3.1.4 USE OF INTEGRITY IN THE INDUSTRY
AND ACADEMIA 227
9.3.2 CHEMBIOBANK 228
9.3.3 MOLECULAR LIBRARIES PROGRAM 231
9.4 KNOWLEDGE-BASED DATA MINING TECHNOLOGIES 232
9.4.1 PROBLEM TRANSFORMATION METHODS 233
9.4.2 ALGORITHM ADAPTATION METHODS 234
CONTENTS I XI
9.4.3 TRAINING A MECHANISM OF ACTION MODEL 235
9.5 FUTURE TRENDS AND OUTLOOK 236
REFERENCES 237
10 APPLICATIONS OF RULE-BASED METHODS TO DATA MINING
OF POLYPHARMACOLOGY DATA SETS 241
NATHALIE JULLIAN, YANNIC TOGNETTI, AND MOHAMMAD AFCHAR
10.1 INTRODUCTION 241
10.2 MATERIALS AND METHODS 243
10.2.1 DATA SET PREPARATION 243
10.2.2 PREPARATION OF THE A-1 BINDERS DATA SET 243
10.2.3 ASSOCIATION RULES 246
10.2.4 NOVEL HYBRID STRUCTURES BY FRAGMENT SWAPPING 247
10.3 RESULTS 248
10.3.1 RULES GENERATION AND EXTRACTION 248
10.3.1.1 RULES DESCRIBING THE POLYPHARMACOLOGY SPACE 248
10.3.1.2 OPTIMIZATION OF CR-1 WITH SELECTIVITY OVER D2 249
10.3.1.3 OPTIMIZATION OF CR-1 WITH SELECTIVITY OVER D2 AND 5HT2 250
10.4 DISCUSSION 252
10.5 CONCLUSIONS 254
REFERENCES 254
11 DATA MINING USING LIGAND PROFILING AND TARGET FISHING 257
SHARON D. BRYANT AND THIERRY LONGER
11.1 INTRODUCTION 257
11.2 IN SILICO LIGAND PROFILING METHODS 258
11.2.1 STRUCTURE-BASED LIGAND PROFILING USING MOLECULAR DOCKING 259
11.2.2 STRUCTURE-BASED PHARMACOPHORE PROFILING 260
11.2.3 THREE-DIMENSIONAL BINDING SITE SIMILARITY-BASED PROFILING 262
11.2.4 PROFILING WITH PROTEIN-LIGAND FINGERPRINTS 263
11.2.5 LIGAND DESCRIPTOR-BASED IN SILICO PROFILING 264
11.3 SUMMARY AND CONCLUSIONS 265
REFERENCES 265
PART FOUR SYSTEM BIOLOGY APPROACHES 271
12 DATA MINING OF LARGE-SCALE MOLECULAR AND ORGANISMAL TRAITS
USING AN INTEGRATIVE AND MODULAR ANALYSIS APPROACH 273
SVEN BERGMANN
12.1 RAPID TECHNOLOGICAL ADVANCES REVOLUTIONIZE QUANTITATIVE
MEASUREMENTS IN BIOLOGY AND MEDICINE 273
12.2 GENOME-WIDE ASSOCIATION STUDIES REVEAL QUANTITATIVE TRAIT LOCI 273
12.3 INTEGRATION OF MOLECULAR AND ORGANISMAL PHENOTYPES IS REQUIRED
FOR UNDERSTANDING CAUSATIVE LINKS 275
XII
| CONTENTS
12.4 REDUCTION OF COMPLEXITY OF HIGH-DIMENSIONAL PHENOTYPES
IN TERMS OF MODULES 277
12.5 BIDUSTERING ALGORITHMS 278
12.6 PING-PONG ALGORITHM 280
12.7 MODULE COMMONALITIES PROVIDE FUNCTIONAL INSIGHTS 281
12.8 MODULE VISUALIZATION 282
12.9 APPLICATION OF MODULAR ANALYSIS TOOLS FOR DATA MINING OF
MAMMALIAN DATA SETS 283
12.10 OUTLOOK 287
REFERENCES 288
13 SYSTEMS BIOLOGY APPROACHES FOR COMPOUND TESTING 291
ALAIN SEWER,
JULIA HOENG, RENEE DEEHAN,
JURJEN W. WESTRA,
FLORIAN
MARTIN, TY M. THOMSON, DAVID A. DRUBIN, AND MANUEL C. PEITSCH
13.1 INTRODUCTION 291
13.2 STEP 1: DESIGN EXPERIMENT FOR DATA PRODUCTION 293
13.3 STEP 2: COMPUTE SYSTEMS RESPONSE PROFILES 296
13.4 STEP 3: IDENTIFY PERTURBED BIOLOGICAL NETWORKS 300
13.5 STEP 4: COMPUTE NETWORK PERTURBATION AMPLITUDES 304
13.6 STEP 5: COMPUTE THE BIOLOGICAL IMPACT FACTOR 308
13.7 CONCLUSIONS 311
REFERENCES 312
INDEX 317 |
| any_adam_object | 1 |
| building | Verbundindex |
| bvnumber | BV041107409 |
| classification_rvk | VC 6250 VS 5350 |
| ctrlnum | (OCoLC)863647562 (DE-599)DNB1033857696 |
| dewey-full | 615.10285 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 615 - Pharmacology and therapeutics |
| dewey-raw | 615.10285 |
| dewey-search | 615.10285 |
| dewey-sort | 3615.10285 |
| dewey-tens | 610 - Medicine and health |
| discipline | Chemie / Pharmazie Medizin |
| format | Book |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV041107409 |
| illustrated | Illustrated |
| indexdate | 2024-08-03T00:45:19Z |
| institution | BVB |
| isbn | 9783527329847 9783527655984 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026083689 |
| oclc_num | 863647562 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-20 |
| owner_facet | DE-355 DE-BY-UBR DE-20 |
| physical | XXII, 323 S. 15 farb. Ill. 240 mm x 170 mm |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | Wiley-VCH |
| record_format | marc |
| series | Methods and Principles in Medicinal Chemistry |
| series2 | Methods and Principles in Medicinal Chemistry |
| spelling | Data Mining in Drug Discovery ed. by Rémy D. Hoffmann ... Weinheim Wiley-VCH 2014 XXII, 323 S. 15 farb. Ill. 240 mm x 170 mm txt rdacontent n rdamedia nc rdacarrier Methods and Principles in Medicinal Chemistry 57 Data Mining (DE-588)4428654-5 gnd rswk-swf Arzneimittelforschung (DE-588)4003120-2 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Arzneimittelforschung (DE-588)4003120-2 s Data Mining (DE-588)4428654-5 s DE-604 Hoffmann, Rémy D. Sonstige oth Erscheint auch als Online-Ausgabe, EPUB 978-3-527-65600-4 Erscheint auch als Online-Ausgabe, MOBI 978-3-527-65599-1 Erscheint auch als Online-Ausgabe, PDF 978-3-527-65601-1 Methods and Principles in Medicinal Chemistry 57 (DE-604)BV035418617 57 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4304420&prov=M&dok_var=1&dok_ext=htm Inhaltstext DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026083689&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Data Mining in Drug Discovery Methods and Principles in Medicinal Chemistry Data Mining (DE-588)4428654-5 gnd Arzneimittelforschung (DE-588)4003120-2 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4003120-2 (DE-588)4143413-4 |
| title | Data Mining in Drug Discovery |
| title_auth | Data Mining in Drug Discovery |
| title_exact_search | Data Mining in Drug Discovery |
| title_full | Data Mining in Drug Discovery ed. by Rémy D. Hoffmann ... |
| title_fullStr | Data Mining in Drug Discovery ed. by Rémy D. Hoffmann ... |
| title_full_unstemmed | Data Mining in Drug Discovery ed. by Rémy D. Hoffmann ... |
| title_short | Data Mining in Drug Discovery |
| title_sort | data mining in drug discovery |
| topic | Data Mining (DE-588)4428654-5 gnd Arzneimittelforschung (DE-588)4003120-2 gnd |
| topic_facet | Data Mining Arzneimittelforschung Aufsatzsammlung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=4304420&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026083689&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV035418617 |
| work_keys_str_mv | AT hoffmannremyd dataminingindrugdiscovery |