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The model applicability domain was checked by the ISIDA software to verify prediction reliability.
The model applicability domain was always verified by the leverage approach in order to propose reliable predicted data.
An outlier detection method was also introduced to determine the model applicability domain and diagnose the outliers in the gathered dataset.
Particular attention is devoted to the model Applicability Domain, using different approaches such as Descriptor Range, Leverage, and Principal Component Analysis (PCA) of the modeling molecular descriptors, in order to discriminate between interpolated and extrapolated predictions.
We evaluated classifier fusion and model applicability domain (MAD) considerations for this dataset.
In any case, the goal of model maintenance is to increase the overall quality of the predictions and expand the model applicability domain.
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These models are more reliable and give better predictions for chemicals that lie in the model applicability domains.
The need to compare compound datasets arises from various scenarios, like mergers, library extension programs, gap analysis, combinatorial library design, or estimation of QSAR model applicability domains.
A potential user is required to make a comparison of model applicability domains and their predictivity for a given activity in order to decide if the model can make reliable predictions for a given chemical compound.
Potential derivatives with higher predicted activity values than the template were identified and a detailed analysis on the models applicability domain defined the designed compounds, whose estimations can be accepted with confidence.
Figure 4 GP determine models applicability domain.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com