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Regression models, statistical/probabilistic

Simplistic and heuristic similarity-based approaches can hardly produce as good predictive models as modern statistical and machine learning methods that are able to assess quantitatively biological or physicochemical properties. QSAR-based virtual screening consists of direct assessment of activity values (numerical or binary) of all compounds in the database followed by selection of hits possessing desirable activity. Mathematical methods used for models preparation can be subdivided into classification and regression approaches. The former decide whether a given compound is active, whereas the latter numerically evaluate the activity values. Classification approaches that assess probability of decisions are called probabilistic. [Pg.25]


See other pages where Regression models, statistical/probabilistic is mentioned: [Pg.145]    [Pg.1811]    [Pg.745]    [Pg.194]    [Pg.421]    [Pg.430]    [Pg.30]    [Pg.40]    [Pg.3]   


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