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Fig. 1 Overview of our deep learning framework for predicting miRNA targeted by small molecules in the presence of sparse and small-size chemical datasets.a sChemNET integrates labeled and unlabeled chemical structure information to predict bioactive small molecules against miRNAs or their mRNA targets. (Left) Labeled small molecules (sky-blue) are known to affect the expression level of miRNAs or their mRNA targets, as curated in the SM2miR database. The dotted arrow represents the experimentally verified small molecule-miRNA association and the up and down-arrows (in green and red, respectively) represent whether there is up-or down-regulation of the expression level (Right). The Drug Repurposing Hub database was used to obtain thousands of small molecules yet unknown to affect miRNAs (a.k.a. unlabeled) shown in green. b sChemNET is a two-layered, fully connected neural network model that incorporates unlabeled chemical structure information during training to enhance prediction performance when only a small set of bioactive small molecule-miRNA dataset is available for training. The trained sChemNET model provides predicted scores for each miRNA given a small molecule’s chemical fingerprint (obtained from its 2D chemical structure representation). Nodes represent input chemical features (yellow), hidden units (gray), and miRNAs’ predicted scores represent the output (purple). Solid lines show the connection between the layers. Molecules with known bioactivity, labeled, molecules without a bioactivity designation, unlabeled. Different miRNAs are illustrated with different colors.