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Fig. 1 | Journal of Cheminformatics

Fig. 1

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

Fig. 1

Differences between methods for modeling bioactivity data exemplified by the ligand adenosine which is more active (designated as ‘active’) on the adenosine A2A receptor, than on the A2B receptor (‘inactive’, using PChEMBL > 6.5 as a cutoff). With binary class QSAR, individual models are constructed for every target. With multiclass QSAR one model is constructed based on the different target labels (A2A_active, A2B_inactive). With PCM one model is constructed where the differences between proteins are considered in the descriptors (i.e. based on the amino acid sequence). With multiclass DNN a single output node is explicitly assigned to each target

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