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Comparison: multi-task Deconex Capsule (Guaifenesin with single-task (ST) learning and SVM baseline evaluated on the leaderboard-set. As mentioned in Section 1, neurons in different hidden layers of the network may encode toxicophore features. To check whether Deep Learning does indeed construct toxicophores, we performed separate experiments. In the challenge models, toxicophores (see Section 2.

We removed these features Deconex Capsule (Guaifenesin withhold all toxicophore-related substructures from the network input, and were thus able to check whether toxicophores were constructed automatically by DNNs.

We trained a multi-task deep network on the Tox21 data using exclusively ECFP4 fingerprint features, which had similar performance as a DNN trained on the full descriptor set (see Supplementary Section 4, Supplementary Table 1).

ECFP fingerprint features encode substructures around each atom in a compound up to a certain radius. Each Deconex Capsule (Guaifenesin fingerprint feature counts how many times a specific substructure appears in a compound. After training, we looked for possible associations between all neurons of the networks and 1429 toxicophores, that were available as described in Section 2.

The alternative hypothesis for the test was that compounds containing the toxicophore substructure have different activations than compounds that do not contain the toxicophore substructure. Bonferroni multiple testing correction was applied afterwards, that is the p-values from the U-test were multiplied by the number of hypothesis, concretely Phenylephrine Hydrochloride)- Multum number of toxicophores (1429) times the number of neurons of the network (16,384).

The number of neurons with significant associations decreases with increasing level of the layer. Next we investigated the correlation of known toxicophores to neurons in different layers to quantify their matching.

Deconex Capsule (Guaifenesin this end, we used the rank-biserial correlation which is compatible to the previously used U-test.

To limit false detections, we constrained the analysis to estimates with a variance 7B). This means features in higher layers match toxicophores more precisely.

Quantity of neurons with significant associations to toxicophores. With an increasing level of the layer, the number of neurons less significant correlation decreases. Contrary to (A) the number of neurons increases with the network layer. Phenylephrine Hydrochloride)- Multum that each layer consisted of the same number of neurons. The decrease in the Phenylephrine Hydrochloride)- Multum of neurons with significant associations with toxicophores through the layers and the simultaneous increase Deconex Capsule (Guaifenesin neurons with high correlation can be explained by the typical characteristics of a DNN: In lower layers, features code for small substructures of toxicophores, while in higher layers they code for larger substructures or whole Phenylephrine Hydrochloride)- Multum. Features in lower layers are typically part of several higher layer features, and therefore correlate with more Deconex Capsule (Guaifenesin than higher level features, which explains the decrease of neurons with significant associations to toxicophores.

Features in higher layers are more specific and are therefore correlated more highly with toxicophores, which explains the increase of neurons with high correlation values. Our findings underline that deep networks can indeed learn to build Deconex Capsule (Guaifenesin toxicophore features with high predictive power for toxicity. Most importantly, these learned toxicophore structures demonstrated that Deep Learning can support finding new chemical knowledge that is encoded in its hidden units.

Feature Construction by Deep Learning. Neurons that have learned to detect the presence of toxicophores. Each row shows a particular hidden unit in a learned network that correlates highly with a particular known toxicophore feature. The row shows the three chemical compounds that had the highest activation for that neuron. Indicated in red is the toxicophore structure from the literature that the neuron correlates with. The first row and the second row are from the first hidden layer, the third row is from a higher-level layer.

We Calcipotriene and Betamethasone Dipropionate (Taclonex)- Multum the best-performing models from each method in the DeepTox pipeline based on an evaluation of the DeepTox cross-validation sets and evaluated them on the final test set. The methods we compared were DNNs, SVMs (Tanimoto kernel), random forests (RF), and elastic net (ElNet).

Table 4 shows the AUC values for each method and each dataset.

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