The method was bench marked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for “anonymous” compounds or compounds not present in the training set.

The AUC was excellent when using drug names and scores as input feature for the predictions.

When only the scores of the predictions were used (that is, treating the compound as anonymous), there was a clear decrease in the AUC suggesting that the method performs better for already known chemical entities rather than for new unseen compounds.

The RFC correctly recalled 66% of the pairs with a precision of 0.73 and an AUC of 0.71 using a 10-fold cross validation (Table 1).

However, by using the DrugBank ID as an input feature, the accuracy of nAnnoLyze dramatically improves to a 0.93 precision, 0.93 recall and a 0.97 of AUC (Fig.

Using ROC analysis (1ms timescale, 10-fold cross-validated), we find that spikes are predicted with an area-under-the-curve ( AUC ) of 0.71:0.01 for Model 1, while Model 2 yields 0.72:0.01, 0.74:0.01, and 0.77:0.01 as the input amplitude increases from 0.5 a, to 1.00 a, to 1.5 a.

However, Model 2 provides better spike prediction ( AUC ) in all cases (paired t-tests, p = 0.01, p<10_5, p<10_3).

With few inputs, the post-synaptic firing is dominated by post-spike effects and timing of postsynaptic spikes is predicted relatively inaccurately ( AUC = 0.78 for this example with 4 inputs).

With an increasing fraction of inputs included in the model, their contribution to spike prediction increases, and timing of individual spikes is predicted with progressively increasing accuracy ( AUC = 0.82, 0.86, and 0.98 for 256, 512, and 1024 inputs, respectively).

B) Model accuracy: Receiver operating characteristic (ROC) curves for the example cell and area under the curve ( AUC ) for all cells from A. Curves show the cross-validated false positive rate (FPR) vs true positive rate (TPR) for spike detection in 1ms bins.