Carbo-Dorca / Robert / Amat Molecular Quantum Similarity in QSAR and Drug Design

2000
ISBN: 978-3-642-57273-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 73, 123 Seiten, eBook

Reihe: Lecture Notes in Chemistry

Zielgruppe

Research

Autoren/Hrsg.

Carbo-Dorca, R.

Robert, D.

Amat, L.

Girones, X.

Besalu, E.

Inhaltsverzeichnis

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.- 2.1.1 Boolean tagged sets.- 2.1.2 Functional tagged sets.- 2.1.3 Vector semispaces.- 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).- 2.6.1 Overlap MQSM.- 2.6.2 Coulomb 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).- 2.9.1 The Carbó index.- 2.10 The Atomic Shell Approximation (ASA).- 2.10.1 Promolecular ASA.- 2.10.2 ASA parameters optimization procedure.- 2.10.3 Example of ASA fitting: adjustment to ab initio atomic densities using a 6-31 IG basis set.- 2.10.4 Descriptive capacity of ASA.- 2.11 The molecular alignment problem.- 2.11.1 Dependence of MQSM with the relative orientation between two molecules.- 2.11.2 Maximal similarity superposition algorithm.- 2.11.3 Common skeleton recognition: the topo-geometrical superposition algorithm.- 2.11.4 Other molecular alignment methods.- 3 Application of Quantum Similarity to QSAR.- 3.1 Theoretical connection between QS and QSAR.- 3.1.1 Beyond the expectation value.- 3.2 Construction of the predictive model.- 3.2.1 Multilinear regression.- 3.3 Possible alternatives to the multilinear regression.- 3.3.1 Partial least squares (PLS) regression.- 3.3.2 Neural Network algorithms.- 3.4 Parameters to assess the goodness-of-fit.- 3.4.1 The multiple determination coefficient r2.- 3.4.2 The standard deviation coefficient ?N.- 3.5 Robustness of the model.- 3.5.1 Cross-validation by leave-one-out.- 3.5.2 The prediction coefficient q2.- 3.5.3 Influence on the regression results.- 3.6 Study of chance correlations.- 3.6.1 The randomization test.- 3.7 Comparison between the QSAR models based on MQSM and other 2D and 3D QSAR methods.- 3.7.1 Comparison with 2D methods.- 3.7.2 Comparison with 3D methods built on grids.- 3.8 Limitations of the models based on MQSM.- 3.8.1 Homogeneity of the sets.- 3.8.2 The problem of the bioactive conformation.- 3.8.3 Determination of molecular alignment.- 4 Full molecular quantum similarity matrices as QSAR descriptors.- 4.1 Pretreatment for quantum similarity matrices.- 4.1.1 Dimensionality reduction.- 4.1.2 Variable selection.- 4.2 The MQSM-QSAR protocol.- 4.3 Combination of quantum similarity matrices: the tuned QSAR model.- 4.3.1 Mixture of matrices and coefficient constraints.- 4.3.2 Optimization of the convex coefficients.- 4.4 Examples of QSAR analyses from quantum similarity matrices.- 4.4.1 Activity of indole derivatives.- 4.4.2 Aquatic toxicity of substituted benzenes.- 4.4.3 Single-point mutations in the subtilisin enzyme.- 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.- 5.2.1 QS-SM as an alternative to log P values.- 5.2.2 QS-SM as an alternative to Hammett a constant.- 5.3 Description of biological activities using fragment QS-SM.- 5.3.1 Activity against Bacillus cereus ATCC 11778 (Bc).- 5.3.2 Activity against Streptococcus faecalis ATCC 10541 (Sf).- 5.3.3 Activity against Staphylococcus aureus ATCC 25178 (Sa).- 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 descriptor for simple linear QSAR models.- 6.3 Evaluation of molecular properties using ?Vee? as a descriptor.- 6.3.1 Inhibition of spore germination by aliphatic alcohols.- 6.3.2 Inhibition of microbial growth by aliphatic alcohols and amines.- 6.3.3 Aquatic toxicity of benzene-type compounds.- 6.3.4 Activity of alkylimidazoles.- 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.- 7.2.1 Nuclear density functions: the Skyrme-Hartree-Fock model.- 7.3 Structure-property relationships in nuclei.- 7.3.1 The nuclear data set.- 7.3.2 The binding energy per nuclcon.- 7.3.3 The mass excess.- 7.4 Limitations of the approach.- References.