SAR Classification Probabilistic Models

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. 

Various classification approaches have been reported to be used successfully in conjunction with fragment descriptors for building classification SAR models the Linear Discriminant Analysis (LDA), the Partial Least Square Discriminant Analysis (PLS-DA), Soft Independent Modeling by Class Analogy (SIMCA), Artificial Neural Networks (ANN), ° Support Vector 

Numerous studies have been devoted to classification (probabilistic) approaches used in conjunction with fragment descriptors for virtual screening. Here we present several examples. 

Harper et al. have demonstrated a much better performance of probabilistic binary kernel discrimination method to screen large databases compared to 

Benchmarking studies on various biological and physicochemical properties 307,312 QSAR/QSPR models for involving fragment descriptors 

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