MicroRNAs (miRNAs) play key roles in human diseases, including cancer and infectious diseases, by regulating gene expression. Modulating miRNAs or their gene targets with small molecules present a potential therapeutic approach for correcting disease-related cellular dysfunctions. However, predicting effective small molecules for specific miRNAs is difficult due to limited data on miRNA-small molecule interactions. Although therapeutic oligonucleotides targeting miRNAs have shown promise, challenges in delivery, stability, and toxicity remain. Small molecule targeting offers an alternative, yet the principles governing small molecule activity against miRNAs are still being explored, limiting predictive capabilities.
Researchers developed sChemNET, a deep-learning framework to predict small molecules capable of modulating miRNA bioactivity. Unlike prior models limited to known small molecule-miRNA pairs, sChemNET utilizes chemical structures to identify bioactive compounds across diverse chemical libraries. By integrating chemical and miRNA sequence information, sChemNET can predict small molecules influencing miRNAs, even for limited datasets or across species. It highlighted vitamin D’s effects on breast cancer-related miRNAs, demonstrating its potential for broad miRNA targeting applications.
The study leveraged the SM2miR database to compile a dataset of small molecule and miRNA associations, specifically drawing from Homo sapiens, Mus musculus, and Rattus norvegicus. Small molecules in this dataset were mapped to PubChem CIDs, and miRNAs were linked to miRBase identifiers. A total of 4,244 interactions across 18 species were gathered, filtering each organism’s dataset to miRNAs with at least five small molecule interactions. Additional associations were identified through RNAInter, adding 1,180 new small molecule-miRNA pairs for humans. The Drug Repurposing Hub provided a library of small molecules with no known miRNA interactions for baseline compounds, creating a comprehensive test set for various organisms. Chemical structures were represented by MACCS fingerprints, computed through RDKit, to ensure consistent structural characterization.
A model called sChemNET was developed to predict small molecule-miRNA interactions. Depending on the task, it employed a two-layered neural network to map chemical structures to miRNA targets, trained with or without miRNA sequence data. Hyperparameters such as dropout, hidden units, learning rate, and regularization were fine-tuned through Bayesian optimization, with Leave-One-Out cross-validation (LOOCV) used to evaluate predictive accuracy. In parallel, baseline methods included chemical similarity scoring, random assignment, and machine learning classifiers such as Random Forest and XGBoost, offering comparative insights into model performance. Finally, sChemNET’s effectiveness was validated on a prospective test set, utilizing RNAInter-derived interactions for performance assessment, with additional analyses on drug mechanisms and enrichment.
sChemNET is a deep learning framework designed to predict drug targets for small chemical datasets, specifically focusing on small molecules that affect miRNAs and their biological targets. Combining labeled (bioactive) and unlabeled small molecule data, sChemNET builds a neural network that learns from chemical structures to predict their impact on miRNAs. In testing, sChemNET effectively identified bioactive molecules for miRNAs across several species, outperforming baseline models, even with chemically diverse datasets. This framework was further validated experimentally, demonstrating its predictive ability for drug-miRNA interactions, including for drugs like docetaxel affecting miR-451 in zebrafish models.
In conclusion, Proteins are the main targets in pharmaceuticals, yet many disease-related proteins remain untreatable. This study explores targeting RNA, particularly microRNAs (miRNAs), as an alternative. Despite understanding miRNA-disease links, miRNA-based drugs are yet to be approved. This study introduces sChemNET, a deep learning model predicting small molecules that may impact miRNA function, validated on zebrafish embryos and human cells. sChemNET’s predictions support drug repurposing, particularly for cancers, and suggest future exploration with FDA-approved drugs or other chemical libraries.
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