Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

卷:257

Review

关键词: GENE-EXPRESSION SIGNATURES;    SEQUENCE-BASED DESIGN;    NONCODING RNAS;    MIRNA ASSOCIATIONS;    CRYOELECTRON MICROSCOPY;    INTERACTION PREDICTION;    CHEMICAL-MODIFICATION;    CONNECTIVITY MAP;    NEURAL-NETWORK;    CRYO-EM;   

DOI  :  10.1016/j.ejmech.2023.115500

来源: SCIE

【 摘 要 】

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their post -transcriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of al-gorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

【 授权许可】

Free