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Chemistry - Theoretical and Computational Chemistry | Molecular Quantum Similarity in QSAR and Drug Design

Molecular Quantum Similarity in QSAR and Drug Design

Series: Lecture Notes in Chemistry, Vol. 73

Carbo-Dorca, R., Robert, D., Amat, L., Girones, X., Besalu, E.

Softcover reprint of the original 1st ed. 2000, XII, 123 p.

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  • About this book

The authors introduce the concept of Molecular Quantum Similarity, developed in their laboratory, in a didactic form. The basis of the concept combines quantum theoretical calculations with molecular structure and properties even for large molecules. They give definitions and procedures to compute similarities molecules and provide graphical tools for visualization of sets of molecules as n-dimensional point charts.

Content Level » Research

Keywords » Density Functions - Molecular Similarity - Similarity Indices - Visualization of Molecular Point Clouds - n-Dimensional Representation

Related subjects » Applications - Complexity - Theoretical and Computational Chemistry

Table of contents 

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 Carb├│ 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.

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