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Each test set compound was represented by a single random low-energy conformer.
The optimal assignment step maps each atom of the query compound onto an atom of the data set compound.
Note that st* and γt*are characteristic of each training set compound contributing to the model of interest.
Analogously, this applies to the j-th column of S and the j-th atom of the data set compound.
The final score was calculated based on the types of the NNs (active or inactive), to arrive at the prediction score for each evaluation set compound.
In kNN calculations, each evaluation set compound is compared to all training set compounds and the top k compounds with highest T values were selected as the nearest neighbours (NNs).
Similar(48)
The conformation generation for test set compounds was carried out in a similar way like training set compounds using conformation analysis algorithm.
In addition, in the GPCR, and PI data set compounds were described by their physicochemical properties.
Figure 11 Visualizing the selected test set compounds of the Caco-2 dataset.
The structures of the training set compounds were shown in Fig. 2.
Out of 19,676 test set compounds, 16,344 test compounds were single-label and 3,332 test compounds were multi-label.
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CEO of Professional Science Editing for Scientists @ prosciediting.com