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Fig. 5 | Journal of Cheminformatics

Fig. 5

From: Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set

Fig. 5

Average performance of the individual DNN grouped per method, architecture and descriptors. Average value is shown for all models trained sharing a setting indicated on the x-axis, error bars represent the SEM of that average. Black bars on the left represent the ensemble methods (average value and majority vote). Grey bars on the right indicate the previous best performing DNN (DNN_PCM), NB with activity cut-off at 6.5 log units and z-score calculation, and default NB with activity cut-off at 10 μM. We observed PCM to be the best way to model the data (green bars), architecture 3 to be the best performing (blue bars), and usage of 4096 bit descriptors with additional physicochemical property descriptors to perform the best (red bars). Using ensemble methods further improves performance (black bars)

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