Biological activity experimental data

Finally, more and more experimental data must be collected in order to understand the biological (including antitumor) activity of organotin(IV) complexes... 

Prediction of ADME properties should be simple, since the number of descriptors underlying the properties is relatively small, compared to the number associated with effective drug-receptor binding space. In fact, prediction of ADME is difficult The current ADME experimental data reflect a multiplicity of mechanisms, making prediction uncertain. Screening systems for biological activity are typically single mechanisms, where computational models are easier to develop [1],... 

Among many theoretical approaches, the quantitative structure-property/ activity relationships (QSPR/QSAR) methods in conjunction with experimental data pave the way to characterization of properties of new compounds. Properly calibrated such methods provide tools for the prediction of physicochemical parameters (QSPR) and/or biological activity (QS AR) for substances, which have not been yet examined in experiments (Wiener, 1947a, b, 1948a, b Randic and Basak, 1999, 2001 Randic and Pompe, 2001a, b Basak et al., 2001). 

Chemoinformatics refers to the systems and scientific methods used to store, retrieve, and analyze the immense amount of molecular data that are generated in modern drug-discovery efforts. In general, these data fall into one of four categories structural, numerical, annotation/text, and graphical. However, it is fair to say that the molecular structure data are the most unique aspect that differentiate chemoinformatics from other database applications (1). Molecular structure refers to the 1-, 2-, or 3-D representations of molecules. Examples of numerical data include biological activity, p/C, log/5, or analytical results, to name a few. Annotation includes information such as experimental notes that are associated with a structure or data point. Finally, any structure... 

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