Buch, Englisch, Band 73, 123 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 224 g
Reihe: Lecture Notes in Chemistry
Buch, Englisch, Band 73, 123 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 224 g
Reihe: Lecture Notes in Chemistry
ISBN: 978-3-540-67581-5
Verlag: Springer Berlin Heidelberg
s,theHosoyaandRandicindices, ortheKier'sconnectivities,amongseveralnotsowellknownnumericaldataare usual reference descriptors. They are putatthe researchers' disposition,andare easily deducible from any molecular representation in form ofordered setsof numerical figures. All ofthem are profusely studied and employed in present times. The main idea consists into the useofthese numerical data in orderto obtaininformationonthemoleculartrendstopossessoracquirecertainproperties and, even better than this, to determine in which degree or intensity molecules presenteverything.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Naturwissenschaften Chemie Chemie Allgemein Chemische Labormethoden, Stöchiometrie
- Naturwissenschaften Chemie Chemie Allgemein Pharmazeutische Chemie, Medizinische Chemie
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Naturwissenschaften Chemie Chemie Allgemein Toxikologie, Gefahrstoffe, Sicherheit in der Chemie
- Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
- Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Kosmetische Technologie
- Naturwissenschaften Chemie Chemie Allgemein Chemometrik, Chemoinformatik
- Naturwissenschaften Chemie Physikalische Chemie Quantenchemie, Theoretische Chemie
- Mathematik | Informatik Mathematik Mathematik Interdisziplinär Computeralgebra
Weitere Infos & Material
1 Introduction.- 1.1 Origins and evolution of QSAR.- 1.2 Molecular similarity in QSAR.- 1.3 Scope and contents of the book.- 2 Quantum objects, density functions and quantum similarity measures.- 2.1 Tagged sets and molecular description.- 2.2 Density functions.- 2.3 Quantum objects.- 2.4 Expectation values in Quantum Mechanics.- 2.5 Molecular Quantum Similarity.- 2.6 General definition of molecular quantum similarity measures (MQSM).- 2.7 Quantum self-similarity measures.- 2.8 MQSM as discrete matrix representations of the quantum objects.- 2.9 Molecular quantum similarity indices (MQSI).- 2.10 The Atomic Shell Approximation (ASA).- 2.11 The molecular alignment problem.- 3 Application of Quantum Similarity to QSAR.- 3.1 Theoretical connection between QS and QSAR.- 3.2 Construction of the predictive model.- 3.3 Possible alternatives to the multilinear regression.- 3.4 Parameters to assess the goodness-of-fit.- 3.5 Robustness of the model.- 3.6 Study of chance correlations.- 3.7 Comparison between the QSAR models based on MQSM and other 2D and 3D QSAR methods.- 3.8 Limitations of the models based on MQSM.- 4 Full molecular quantum similarity matrices as QSAR descriptors.- 4.1 Pretreatment for quantum similarity matrices.- 4.2 The MQSM-QSAR protocol.- 4.3 Combination of quantum similarity matrices: the tuned QSAR model.- 4.4 Examples of QSAR analyses from quantum similarity matrices.- 5 Quantum self-similarity measures as QSAR descriptors.- 5.1 Simple QSPR models based on QS-SM.- 5.2 Characterization of classical 2D QSAR descriptors using QS-SM.- 5.3 Description of biological activities using fragment QS-SM.- 6 Electron-electron repulsion energy as a QSAR descriptor.- 6.1 Connection between the electron-electron repulsion energy and QS-SM.- 6.2 ?Vee? as a descriptorfor simple linear QSAR models.- 6.3 Evaluation of molecular properties using ?Vee? as a descriptor.- 7 Quantum similarity extensions to non-molecular systems: Nuclear Quantum Similarity.- 7.1 Generality of Quantum Similarity for quantum systems.- 7.2 Nuclear Quantum Similarity.- 7.3 Structure-property relationships in nuclei.- 7.4 Limitations of the approach.- References.
