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The outlying molecules were filtered using p = 0.01.
Thus, the exclusion of outlying molecules, which distorted the training procedures, contributed models with higher prediction accuracies.
In our previous study [11] we suggested that the 691 outlying molecules could be enriched with decomposing structures.
The RMSEs for the PATENTS set were reported for the whole set, i.e. also including the outlying molecules, which were excluded for different p values.
The number of outlying molecules identified for p = 0.1 (N = 21,928) was less than expected for this p value, N = 22,208 for Gaussian distribution.
Table 3 indicates the performances of consensus models and individual sub-models calculated for the different number of excluded outlying molecules as described in the Methods section.
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