ArticleJournal of cheminformatics2012
Computational analysis and predictive modeling of small molecule modulators of microRNA.
Article in Journal of cheminformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 45 citations in OpenAlex.
- SMTRI: A deep learning-based web service for predicting small molecules that target miRNA-mRNA interactions.Molecular therapy. Nucleic acids · 2024Article
- Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications.European journal of medicinal chemistry · 2023Review
- Review
- A message passing framework with multiple data integration for miRNA-disease association prediction.Scientific reports · 2022Article
- Quantitative Structure-Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure.Journal of medicinal chemistry · 2022Article
- Machine learning in the development of targeting microRNAs in human disease.Frontiers in genetics · 2022Review
- Machine and deep learning approaches for cancer drug repurposing.Seminars in cancer biology · 2021Review
- A systematic study of critical miRNAs on cells proliferation and apoptosis by the shortest path.BMC bioinformatics · 2020Article
- Drug repositioning based on the target microRNAs using bilateral-inductive matrix completion.Molecular genetics and genomics : MGG · 2020Article
- In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm.Molecular therapy. Nucleic acids · 2019Article
- Exploration of Machine Learning for Hyperuricemia Prediction Models Based on Basic Health Checkup Tests.Journal of clinical medicine · 2019Article
- Distinct and Modular Organization of Protein Interacting Sites in Long Non-coding RNAs.Frontiers in molecular biosciences · 2018Article
- miRDDCR: a miRNA-based method to comprehensively infer drug-disease causal relationships.Scientific reports · 2017Article
- Identification of associations between small molecule drugs and miRNAs based on functional similarity.Oncotarget · 2016Article
- Computational Analysis and Predictive Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers.PloS one · 2016Article
- Small molecule compounds targeting miRNAs for cancer therapy.Advanced drug delivery reviews · 2015Review
- Cheminformatics models for inhibitors of Schistosoma mansoni thioredoxin glutathione reductase.TheScientificWorldJournal · 2014Article
- Article
- Time for the zebrafish ENCODE.Journal of genetics · 2013Article
- Cheminformatic models based on machine learning for pyruvate kinase inhibitors of Leishmania mexicana.BMC bioinformatics · 2013Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroRNAs (miRNA) are small endogenously transcribed regulatory RNA which modulates gene expression at a post transcriptional level. These small RNAs have now been shown to be critical regulators in a number of biological processes in the cell including pathophysiology of diseases like cancers. The increasingly evident roles of microRNA in disease processes have also motivated attempts to target them therapeutically. Recently there has been immense interest in understanding small molecule mediated regulation of RNA, including microRNA.
resultsWe have used publicly available datasets of high throughput screens on small molecules with potential to inhibit microRNA. We employed computational methods based on chemical descriptors and machine learning to create predictive computational models for biological activity of small molecules. We further used a substructure based approach to understand common substructures potentially contributing to the activity.
conclusionWe generated computational models based on Naïve Bayes and Random Forest towards mining small RNA binding molecules from large molecular datasets. We complement this with substructure based approach to identify and understand potentially enriched substructures in the active dataset. We use this approach to identify miRNA binding potential of a set of approved drugs, suggesting a probable novel mechanism of off-target activity of these drugs. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding RNA binding activities of small molecules and predictive modeling of these activities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.