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Book Details
Abstract
There have been significant developments in the use of knowledge-based expert systems in chemistry since the first edition of this book was published in 2009. This new edition has been thoroughly revised and updated to reflect the advances.
The underlying theme of the book is still the need for computer systems that work with uncertain or qualitative data to support decision-making based on reasoned judgements. With the continuing evolution of regulations for the assessment of chemical hazards, and changes in thinking about how scientific decisions should be made, that need is ever greater. Knowledge-based expert systems are well established in chemistry, especially in relation to toxicology, and they are used routinely to support regulatory submissions. The effectiveness and continued acceptance of computer prediction depends on our ability to assess the trustworthiness of predictions and the validity of the models on which they are based.
Written by a pioneer in the field, this book provides an essential reference for anyone interested in the uses of artificial intelligence for decision making in chemistry.
The author studied chemistry at the University of Manchester before working on the synthesis of novel herbicides and fungicides for Fisons Ltd at Chesterford Park Research Station near Saffron Walden. His PhD at the University of Surrey was on chemical synthesis. He took an interest in knowledge-based computer systems and became Head of Chemical Information and Computing for Schering Agrochemicals Ltd. He was one of the founders of Lhasa Limited, a not-for-profit company specialising in knowledge-based expert systems in chemistry including the widely-used Derek, Meteor, and Zeneth systems for predicting chemical toxicity, metabolism, and chemical degradation. Although semi-retired, he continues to contribute to research and development work at Lhasa Limited in his role as Scientific Advisor and is working in a project on synthetic accessibility led by scientists at the US Niational Institutes of Health. He developed and maintains software for chemical hazard classification and chemical safety data sheet management, Harmoneus and Prometheus, which are supplied by Hibiscus plc. He has published over eighty scientfic papers, posters and book chapters. His hobbies include climbing and caving and he has published articles about international caving expeditions that he has taken part in.
Table of Contents
| Section Title | Page | Action | Price |
|---|---|---|---|
| Cover | Cover | ||
| Preface | v | ||
| Contents | vii | ||
| Chapter 1 Artificial Intelligence - Making Use of Reasoning | 1 | ||
| References | 5 | ||
| Chapter 2 Synthesis Planning by Computer | 6 | ||
| References | 14 | ||
| Chapter 3 Other Programs to Support Chemical Synthesis Planning | 15 | ||
| 3.1 Programs That Are Similar to LHASA in Their Approach | 15 | ||
| 3.1.1 SECS | 15 | ||
| 3.1.2 PASCOP | 16 | ||
| 3.1.3 SYNLMA | 16 | ||
| 3.1.4 SYNCHEM and SYNCHEM2 | 17 | ||
| 3.1.5 SYNGEN | 19 | ||
| 3.1.6 SYNSUP-MB and CAOSP | 21 | ||
| 3.1.7 RESYN | 21 | ||
| 3.1.8 SOS, MARSEIL, CONAN, HOLOWin and GRAAL | 22 | ||
| 3.1.9 AIPHOS, SOPHIA and KOSP | 23 | ||
| 3.1.10 Chiron | 24 | ||
| 3.1.11 PSYCHO | 24 | ||
| 3.1.12 COMPASS | 25 | ||
| 3.1.13 Wipke and Rogers SST | 25 | ||
| 3.1.14 SESAM | 26 | ||
| 3.2 CICLOPS, EROS and WODCA - A Different Approach | 26 | ||
| 3.3 PIRExS | 28 | ||
| 3.4 COSYMA | 29 | ||
| 3.5 Work by Wilcox and Levinson - Automated Rule Discovery | 29 | ||
| 3.6 Predicting Reactions | 31 | ||
| 3.6.1 CAMEO | 31 | ||
| 3.6.2 Work by Chen and Baldi | 31 | ||
| 3.7 What Happened to Synthesis Planning by Computer? | 32 | ||
| References | 35 | ||
| Chapter 4 International Repercussions of the Harvard LHASA Project | 39 | ||
| References | 44 | ||
| Chapter 5 Current Interest in Synthesis Planning by Computer | 46 | ||
| 5.1 Retrosynthetic Analysis | 46 | ||
| 5.1.1 ICSynth | 46 | ||
| 5.1.2 ARChem, RouteDesigner and ChemPlanner | 47 | ||
| 5.1.3 Chematica | 48 | ||
| 5.1.4 Work by Segler, Waller and Preuss | 49 | ||
| 5.1.5 Mining Electronic Laboratory Notebooks | 50 | ||
| 5.1.6 RASA | 51 | ||
| 5.1.7 Use of a Neural Network by Nam and Kim | 52 | ||
| 5.1.8 RetroPath | 52 | ||
| 5.2 Reducing Hazardous Impurities in Pharmaceuticals | 53 | ||
| 5.3 Knowledge-based Systems for Synthetic Accessibility | 53 | ||
| 5.3.1 SPROUT, HIPPO and CAESA | 53 | ||
| 5.3.2 AllChem | 54 | ||
| 5.3.3 RECAP | 54 | ||
| 5.3.4 DOGS | 54 | ||
| 5.3.5 Reactor | 55 | ||
| 5.3.6 Work by Schürer et al. | 55 | ||
| 5.3.7 SAVI | 55 | ||
| 5.3.8 ROBIA | 56 | ||
| 5.4 Other Systems for Synthetic Accessibility and Reaction Prediction | 56 | ||
| 5.4.1 SYLVIA and Work by Boda et al. | 56 | ||
| 5.4.2 SYNOPSIS | 57 | ||
| 5.4.3 IADE | 57 | ||
| 5.4.4 Using Neural Networks | 58 | ||
| 5.4.5 Work by Fukushini et al. | 59 | ||
| 5.4.6 Reaction Predictor | 59 | ||
| 5.4.7 Work by Hristozov et al. | 59 | ||
| 5.4.8 Work by Segler and Waller | 59 | ||
| References | 60 | ||
| Chapter 6 Structure Representation | 64 | ||
| 6.1 Wiswesser Line-formula Notation | 64 | ||
| 6.2 SMILES, SMARTS and SMIRKS | 66 | ||
| 6.3 SYBYL Line Notation (SLN) | 68 | ||
| 6.4 CHMTRN and PATRAN | 69 | ||
| 6.5 ALCHEM | 75 | ||
| 6.6 Molfiles, SDfiles and RDfiles | 75 | ||
| 6.7 Mol2 Files | 76 | ||
| 6.8 The Standard Molecular Data Format and Molecular Information File | 77 | ||
| 6.9 Chemical Markup Language and CMLReact | 77 | ||
| 6.10 CDX and CDXML | 77 | ||
| 6.11 Molecular Query Language (MQL) | 78 | ||
| 6.12 CSRML | 79 | ||
| 6.13 Using Pictures | 80 | ||
| References | 80 | ||
| Chapter 7 Structure, Substructure and Superstructure Searching | 84 | ||
| 7.1 Exact Structure Searching | 84 | ||
| 7.1.1 Canonical SMILES Codes | 85 | ||
| 7.1.2 Morgan Names and SEMA Names | 88 | ||
| 7.1.3 MOLGEN-CID | 92 | ||
| 7.1.4 The Method Described by Henrickson and Toczko | 93 | ||
| 7.1.5 InChI Code | 94 | ||
| 7.1.6 CACTVS Hash Codes | 95 | ||
| 7.2 Atom by Atom Matching | 96 | ||
| 7.3 Substructure Searching | 98 | ||
| 7.4 Set Reduction | 100 | ||
| 7.5 Superstructure and Markush Structure Searching | 104 | ||
| 7.6 Reaction Searching | 105 | ||
| 7.7 Searching for Structures in Wikipedia | 105 | ||
| References | 106 | ||
| Chapter 8 Protons That Come and Go | 108 | ||
| 8.1 Dealing with Tautomerism | 108 | ||
| 8.2 Implicit and Explicit Hydrogen Atoms | 111 | ||
| References | 115 | ||
| Chapter 9 Aromaticity and Stereochemistry | 116 | ||
| 9.1 Aromaticity | 116 | ||
| 9.2 Stereochemistry | 119 | ||
| 9.2.1 Tetrahedral Centres | 119 | ||
| 9.2.2 Double Bonds | 122 | ||
| 9.2.3 Other Kinds of Asymmetry | 124 | ||
| References | 124 | ||
| Chapter 10 DEREK - Predicting Toxicity | 125 | ||
| 10.1 How DEREK Came About | 125 | ||
| 10.2 The Alert-based Approach to Toxicity Prediction in DEREK | 128 | ||
| References | 133 | ||
| Chapter 11 Other Alert-based Toxicity Prediction Systems | 134 | ||
| 11.1 TOX-MATCH and PHARM-MATCH | 134 | ||
| 11.2 Oncologic | 136 | ||
| 11.3 HazardExpert | 138 | ||
| 11.4 BfR/BgVV System | 139 | ||
| 11.5 ToxTree and Toxmatch | 139 | ||
| 11.6 Leadscope Genetox Expert Alerts | 140 | ||
| 11.7 Environmental Toxicity Prediction | 140 | ||
| References | 141 | ||
| Chapter 12 Rule Discovery | 143 | ||
| 12.1 QSAR | 143 | ||
| 12.2 TopKat | 144 | ||
| 12.3 Multicase | 145 | ||
| 12.4 Lazar | 146 | ||
| 12.5 Sarah | 147 | ||
| 12.6 Emerging Pattern Mining | 147 | ||
| 12.7 Other Fragment-based Systems | 149 | ||
| 12.7.1 REX | 149 | ||
| 12.7.2 Using Atom-centred Fragments | 151 | ||
| 12.8 Other Approaches in the Field of Toxicity Prediction | 151 | ||
| 12.9 Discovering Reaction Rules | 152 | ||
| References | 154 | ||
| Chapter 13 The 2D-3D Debate | 158 | ||
| References | 165 | ||
| Chapter 14 Making Use of Reasoning: Derek for Windows | 167 | ||
| 14.1 Moving on from Just Recognising Alerts in Structures | 167 | ||
| 14.2 The Logic of Argumentation | 169 | ||
| 14.3 Choosing Levels of Likelihood for a System Based on LA | 176 | ||
| 14.4 Derek for Windows and Derek Nexus | 178 | ||
| 14.5 The Derek Knowledge Editor | 183 | ||
| 14.6 Making Improvements in the Light of Experience | 187 | ||
| References | 192 | ||
| Chapter 15 Predicting Metabolism | 194 | ||
| 15.1 Predicting Primary Sites of Metabolism | 196 | ||
| 15.1.1 COMPACT | 196 | ||
| 15.1.2 MetaSite and Mass-MetaSite | 197 | ||
| 15.1.3 SPORCalc and MetaPrint2D | 197 | ||
| 15.1.4 SMARTCyp | 197 | ||
| 15.1.5 FAME | 198 | ||
| 15.2 Predicting Metabolic Trees | 198 | ||
| 15.2.1 MetabolExpert | 199 | ||
| 15.2.2 META | 199 | ||
| 15.2.3 TIMES | 200 | ||
| 15.2.4 Meteor | 201 | ||
| References | 207 | ||
| Chapter 16 Relative Reasoning | 211 | ||
| References | 220 | ||
| Chapter 17 Predicting Biodegradation | 221 | ||
| 17.1 BESS | 222 | ||
| 17.2 CATABOL | 223 | ||
| 17.3 The UMBBD, PPS and Mepps | 223 | ||
| 17.4 EnviPath | 226 | ||
| 17.5 CRAFT | 229 | ||
| 17.6 META | 229 | ||
| 17.7 The Future for Prediction of Environmental Degradation | 229 | ||
| References | 230 | ||
| Chapter 18 Other Applications and Potential Applications of Knowledge-based Prediction in Chemistry | 233 | ||
| 18.1 The Maillard Reaction | 233 | ||
| 18.2 Recording Information about Useful Biological Activity | 234 | ||
| 18.3 Proposing Structural Analogues for Drug Design | 235 | ||
| 18.4 Predicting Product Degradation During Storage | 235 | ||
| 18.5 Designing Production Synthesis Routes | 236 | ||
| 18.6 Using Knowledge-based Systems for Teaching | 237 | ||
| References | 238 | ||
| Chapter 19 Combining Predictions | 239 | ||
| 19.1 Introduction | 239 | ||
| 19.2 The ICH M7 Guidelines | 242 | ||
| 19.3 Giving Access to Multiple Models in a Single Package | 243 | ||
| 19.3.1 The OECD (Q)SAR Toolbox | 243 | ||
| 19.3.2 Prediction of Aquatic Toxicity by Gerrit Schüürmann’s Group | 244 | ||
| 19.3.3 Leadscope Model Applier | 245 | ||
| 19.3.4 eTOX and iPiE | 245 | ||
| 19.3.5 Meteor and SMARTCyp | 246 | ||
| 19.3.6 The NoMiracle Project - Mira | 246 | ||
| 19.3.7 Eco-Derek | 248 | ||
| 19.3.8 Derek and Sarah | 249 | ||
| 19.3.9 Combining Predictions Using Dempster-Shafer Theory | 249 | ||
| 19.4 Looking Ahead | 249 | ||
| References | 251 | ||
| Chapter 20 The Adverse Outcome Pathways Approach | 253 | ||
| References | 257 | ||
| Chapter 21 Evaluation of Knowledge-based Systems | 258 | ||
| 21.1 The OECD (Q)SAR Guidelines | 258 | ||
| 21.2 Defining Applicability Domain | 259 | ||
| 21.3 Using Traditional Measures of Predictive Performance | 261 | ||
| 21.4 A Different Way to Evaluate Predictive Performance | 264 | ||
| References | 267 | ||
| Chapter 22 Validation of Computer Predictions | 269 | ||
| References | 272 | ||
| Chapter 23 Artificial Intelligence Developments in Other Fields | 273 | ||
| References | 274 | ||
| Chapter 24 A Subjective View of the Future | 276 | ||
| References | 278 | ||
| Subject Index | 279 |