ArticleBriefings in bioinformatics2024
Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 40 citations in OpenAlex.
- Mammalian therapeutic riboswitches: Engineering and custom ligands.Molecular therapy. Nucleic acids · 2026Review
- DLRNA-BERTa: a transformer approach for predicting molecule-RNA binding affinities.Briefings in bioinformatics · 2026Article
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- MicroRNAs in oncology: a translational perspective in the era of AI.Nature reviews. Clinical oncology · 2026Review
- Advances in computational prediction of RNA-small molecule binding affinity.Journal of computer-aided molecular design · 2026Review
- DeepRNA-DTI: a deep learning approach for RNA-compound interaction prediction with binding site interpretability.Journal of cheminformatics · 2025Article
- Beyond the niche - unlocking the full potential of synthetic riboswitches.Nature communications · 2025Review
- Review
- BioLLMNet: enhancing RNA-interaction prediction with a specialized cross-LLM transformation network.Briefings in bioinformatics · 2025Article
- Data-driven discovery of near-infrared type I photosensitizers for RNA-targeted tumor photodynamic therapy.Chemical science · 2025Article
- Binary curcuminoid complex CRE-Bin inhibits osteoclast differentiation by suppressing the canonical NF-κB signaling pathway and modulating microRNA-223 expression.Scientific reports · 2025Article
- Rebuttal letter of the "comment on "contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction" by Sun and Gao".Bioinformatics (Oxford, England) · 2025Article
- Comment on "Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction" by Sun and Gao.Bioinformatics (Oxford, England) · 2025Article
- Predicting the Toxicity of Drug Molecules with Selecting Effective Descriptors Using a Binary Ant Colony Optimization (BACO) Feature Selection Approach.Molecules (Basel, Switzerland) · 2025Article
- DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.Nucleic acids research · 2025Article
- Advances and Mechanisms of RNA-Ligand Interaction Predictions.Life (Basel, Switzerland) · 2025Review
- Exploring chemical space for "druglike" small molecules in the age of AI.Frontiers in molecular biosciences · 2025Review
- Harnessing Computational Approaches for RNA-Targeted Drug Discovery.RNA nanomed · 2024Article
- DeepRSMA: a cross-fusion-based deep learning method for RNA-small molecule binding affinity prediction.Bioinformatics (Oxford, England) · 2024Article
- Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction.Bioinformatics (Oxford, England) · 2024Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ribonucleic acids (RNAs) play important roles in cellular regulation. Consequently, dysregulation of both coding and non-coding RNAs has been implicated in several disease conditions in the human body. In this regard, a growing interest has been observed to probe into the potential of RNAs to act as drug targets in disease conditions. To accelerate this search for disease-associated novel RNA targets and their small molecular inhibitors, machine learning models for binding affinity prediction were developed specific to six RNA subtypes namely, aptamers, miRNAs, repeats, ribosomal RNAs, riboswitches and viral RNAs. We found that differences in RNA sequence composition, flexibility and polar nature of RNA-binding ligands are important for predicting the binding affinity. Our method showed an average Pearson correlation (r) of 0.83 and a mean absolute error of 0.66 upon evaluation using the jack-knife test, indicating their reliability despite the low amount of data available for several RNA subtypes. Further, the models were validated with external blind test datasets, which outperform other existing quantitative structure-activity relationship (QSAR) models. We have developed a web server to host the models, RNA-Small molecule binding Affinity Predictor, which is freely available at: https://web.iitm.ac.in/bioinfo2/RSAPred/.
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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.