This work provides an introduction to mathematical modeling of molecules and the resulting applications (structure generation, structure elucidation, QSAR/QSPR etc.). Most chemists have experimented with some software that represents molecules in an electronic form, and such models and applications are of increasing interest in diverse and growing fields such as drug discovery, environmental science and metabolomics. Furthermore, structure generation remains the only way to systematically create molecules that are not (yet) present in a database. This book starts with the mathematical theory behind representing molecules, explaining chemical concepts in mathematical terms and providing exercises that can be completed online. The later chapters cover applications of the theory, with detailed explanations on QSPR and QSAR investigations and finally structure elucidation combining mass spectrometry and structure generation. This book is aimed in particular at the users of structure generation methods and corresponding techniques, but also for those interested in teaching and learning mathematical chemistry, and for software designers in chemoinformatics.
Auteur
A.Kerber, R.Laue, U. Bayreuth; M.Meringer, DLR Oberpfaffenhofen; C.Rücker, Leuphana U. Lüneburg; E.Schymanski, EAWAG Dübendorf
Contenu
**Chapter 1: Molecules in silico
**1.1 Graphs, labeled and unlabeled 1.2 Molecular graphs, constitutional isomers 1.3 Summary
**Chapter 2: Substructures, reactions, descriptors
**2.1 Substructures 2.2 Molecular substructures 2.3 Chemical reactions 2.4 Mesomerism 2.5 Existing chemical compounds 2.6 Molecular descriptors 2.7 Summary
**Chapter 3: Chirality
**3.1 Orientation and chirality 3.2 Permutational isomers 3.3 Permutational isomers by content, in particular by racemic content 3.4 Enumeration by symmetry 3.5 Constructive aspects 3.6 Summary
**Chapter 4: Stereoisomers
**4.1 Stereoisomers 4.2 Radon partitions 4.3 Binary Grassmann-Plücker relations 4.4 An example, cyclohexane 4.5 Summary
**Chapter 5: Molecular structure generation
**5.1 Formula based molecular generation 5.2 Reaction based structure generation 5.3 Examples: Combinatorial libraries 5.4 Generic structural formulas 5.5 Example: Patents in chemistry 5.6 Canonizing molecules and graphs 5.7 Data structure for molecular graphs 5.8 Summary
**Chapter 6: Supervised statistical learning
**6.1 Variables and predicting functions 6.2 Models for predicting functions 6.3 Summary
**Chapter 7: Structure-property relationships
**7.1 Optimization of experiments in combinatorial chemistry 7.2 The use of molecular descriptors 7.3 Quantitative structure--property relationships 7.4 Example: Boiling points of alkanes 7.5 Example: Physical density of propyl acrylates 7.6 Example: Antibacterial activity of quinolones 7.7 Remarks on the real library 7.8 Summary
**Chapter 8: Molecular structure elucidation
**8.1 Spectroscopic methods 8.2 The principle of automated molecular structure elucidation 8.3 Basics of mass spectrometry 8.4 Ranking functions for mass spectra 8.5 Classification of mass spectra 8.6 Automated structure elucidation via MS 8.7 High resolution MS 8.8 Molecular formulas from high-resolution MS and MS/MS 8.9 Summary
**Chapter 9: Case studies of CASE
**9.1 Automated structure elucidation with MOLGEN-MS 9.2 Calculated properties to improve CASE 9.3 Examples of CASE at work
**Chapter 10: Appendix
**10.1 Lists of molecular descriptors 10.2 Substructures for MS classifiers 10.3 Molecular formulas, according to mass and ion type 10.4 Isomers, by formula and mass 10.5 Summary