Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein-protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets. Several drug-like compound libraries were screened successfully against different miRNAs in cellular assays further demonstrating the possibility of targeting miRNAs with small molecules. In this review, we summarized the concept and functions of small molecule and miRNAs. Especially, five aspects of miRNA functions were exhibited in detail with individual examples. In addition, four disease states that have been linked to miRNA alterations were summed up. Then, small molecules related to four important miRNAs miR-21, 122, 4644 and 27 were selected for introduction. Some important publicly accessible databases and web servers of the experimentally validated or potential small molecule-miRNA associations were discussed. Identifying small molecule targeting miRNAs has become an important goal of biomedical research. Thus, several experimental and computational models have been developed and implemented to identify novel small molecule-miRNA associations. Here, we reviewed four experimental techniques used in the past few years to search for small-molecule inhibitors of miRNAs, as well as three types of models of predicting small molecule-miRNA associations from different perspectives. Finally, we summarized the limitations of existing methods and discussed the future directions for further development of computational models.

Motivation: It has been shown that microRNAs (miRNAs) play key roles in variety of biological processes associated with human diseases. In Consideration of the cost and complexity of biological experiments, computational methods for predicting potential associations between miRNAs and diseases would be an effective complement. Results: This paper presents a novel model of Inductive Matrix Completion for MiRNA-Disease Association prediction (IMCMDA). The integrated miRNA similarity and disease similarity are calculated based on miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. The main idea is to complete the missing miRNA-disease association based on the known associations and the integrated miRNA similarity and disease similarity. IMCMDA achieves AUC of 0.8034 based on leave-one-out-cross-validation and improved previous models. In addition, IMCMDA was applied to five common human diseases in three types of case studies. In the first type, respectively, 42, 44, 45 out of top 50 predicted miRNAs of Colon Neoplasms, Kidney Neoplasms, Lymphoma were confirmed by experimental reports. In the second type of case study for new diseases without any known miRNAs, we chose Breast Neoplasms as the test example by hiding the association information between the miRNAs and Breast Neoplasms. As a result, 50 out of top 50 predicted Breast Neoplasms-related miRNAs are verified. In the third type of case study, IMCMDA was tested on HMDD V1.0 to assess the robustness of IMCMDA, 49 out of top 50 predicted Esophageal Neoplasms-related miRNAs are verified. Availability and implementation: The code and dataset of IMCMDA are freely available at https://github.com/IMCMDAsourcecode/IMCMDA. Supplementary information: Supplementary data are available at Bioinformatics online.