Molecular Modeling and Prediction of Bioactivity
Klaus Gundertofte, Fleming Steen Jørgensen
Springer US, Jan 31, 2000 - Science - 502 pages
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.
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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 Chemistry Chemometrics cluster combinatorial CoMFA complex computational conformation correlation cross-validation 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 Modeling and Prediction molecular dynamics molecular field Molecular Modeling molecules multivariate neural networks NLE NLE NLE obtained optimization parameters peptides pharmacophore physicochemical plot position potential Prediction of Bioactivity procedure properties protein QSAR QSAR studies 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 validation values variables X-ray zolpidem