Molecular Modeling and Prediction of BioactivityKlaus Gundertofte, Fleming Steen Jørgensen Much of chemistry, molecular biology, and drug design, are centered around the relationships between chemical structure and measured properties of compounds and polymers, such as viscosity, acidity, solubility, toxicity, enzyme binding, and membrane penetration. For any set of compounds, these relationships are by necessity complicated, particularly when the properties are of biological nature. To investigate and utilize such complicated relationships, henceforth abbreviated SAR for structure-activity relationships, and QSAR for quantitative SAR, we need a description of the variation in chemical structure of relevant compounds and biological targets, good measures of the biological properties, and, of course, an ability to synthesize compounds of interest. In addition, we need reasonable ways to construct and express the relationships, i. e. , mathematical or other models, as well as ways to select the compounds to be investigated so that the resulting QSAR indeed is informative and useful for the stated purposes. In the present context, these purposes typically are the conceptual understanding of the SAR, and the ability to propose new compounds with improved property profiles. Here we discuss the two latter parts of the SARlQSAR problem, i. e. , reasonable ways to model the relationships, and how to select compounds to make the models as "good" as possible. The second is often called the problem of statistical experimental design, which in the present context we call statistical molecular design, SMD. 1. |
Contents
Strategies for Molecular Design Beyond the Millennium | 3 |
New Developments and Applications of Multivariate QSAR | 25 |
Test of a General Model for Molecular | 47 |
Copyright | |
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Molecular Modeling and Prediction of Bioactivity Klaus Gundertofte,Fleming Steen Jørgensen Limited preview - 2012 |
Molecular Modeling and Prediction of Bioactivity Klaus Gundertofte,Fleming Steen Jørgensen No preview available - 2012 |
Common terms and phrases
3D-QSAR acceptor active compounds agonists algorithm alignment amino acid amisulpride analysis antagonists approach aromatic atoms binding affinity Biol biological activity calculated cannabinoids carcinogenicity Chem chemical Chemistry Chemometrics cluster coefficient combinatorial CoMFA complex computational conformation correlation cross-validation Cruciani data set database derived developed diversity docking Drug Design electron electrostatic enzyme equation experimental Figure fragments free energy function genetic algorithm grid groups H-bond hydrogen bond hydrophobic indices inhibition inhibitors interactions kcal/mol ligands lipophilicity maps matrix membrane method molecular dynamics molecular field Molecular Modeling molecules multivariate neural networks NLE NLE NLE obtained optimization parameters partition peptides pharmacophore physicochemical plot position potential Prediction of Bioactivity procedure properties protein QSAR QSPR quantitative structure-activity relationships receptor model REFERENCES regions regression representation residues ring screening selection sequence similarity simulations statistical steric substituents SYBYL test set topological training set values variables zolpidem