By Barry A. Bunin, Brian Siesel, Guillermo Morales, Jürgen Bajorath
Chemoinformatics is using details know-how within the acquisition, research and administration of knowledge and data in relation to chemical substances and their homes. the aim of this booklet is to supply computational scientists, medicinal chemists and biologists with whole sensible info and underlying concept on the subject of glossy Chemoinformatics and similar drug discovery informatics applied sciences. this can be a vital guide for choosing the fitting Chemoinformatics approach or expertise to exploit.
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Additional resources for Chemoinformatics: Theory, Practice, & Products
J Am Chem Soc 110:5959–5967.
Thus, conformational analysis often only generates a crude approximation of active conformations. In order to at least partly compensate for these difficulties, information from active sites in target proteins is taken into account, if available (receptor-dependent QSAR). Second, once conformations are modeled, they must be correctly aligned in three dimensions, which is another major source of errors in the system set-up for 3D-QSAR studies. A major milestone for the 3D- (and ultimately multi-dimensional) QSAR field has been the introduction of shape analysis in QSAR (Hopfinger 1980).
9 QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS The major goal of QSAR is the evaluation of molecular features that determine biological activity and the prediction of compound potency as a function of structural modifications. As such QSAR is generally limited to the study of closely related molecules or series of analogs. Since its introduction in the early 1960s the approach has been extensively used in medicinal chemistry to support lead optimization and has become one of the most popular computational methodologies.
Chemoinformatics: Theory, Practice, & Products by Barry A. Bunin, Brian Siesel, Guillermo Morales, Jürgen Bajorath