ArticleJournal of medicinal chemistry2022
Quantitative Structure-Activity Relationship (QSAR) Study Predicts Small-Molecule Binding to RNA Structure.
Article in Journal of medicinal chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
37 citing papers in PubMed.
- Immune cell-derived membrane nanovesicles: A promethean fire for autoimmune disease therapy through immune cell mimicry.Bioactive materials · 2026Review
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Designing small molecules targeting a cryptic RNA binding site through base displacement.Nature chemical biology · 2026Article
- Targeting Non-Coding RNAs as a Potential Therapeutic and Delivery Strategy Against Neurodegenerative Diseases.International journal of molecular sciences · 2026Review
- AI-Augmented Iterative Screening of Libraries Against RNA Targets (AISLAR) Boosts Discovery of SAR-Tractable RNA Binders and Rational Analog Design.Small science · 2026Article
- Synthetic accessibility score as a filter for virtual chemical structures in polymer materials design.Journal of molecular modeling · 2026Article
- From in-silico QSAR modeling to in-vitro MTT assay: experimental validation of novel uPAR leads for triple-negative breast cancer (TNBC) and skin cancer.Scientific reports · 2026Article
- Decoding the lncRNA World: Comprehensive Approaches to lncRNA Structure and Interactome Studies.Cells · 2026Review
- Targeting Non-coding RNAs in Neurodegeneration: Advances in Therapeutic RNA Modalities and Next-Gen Delivery Technologies.Current Alzheimer research · 2026Review
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- DeepRNA-DTI: a deep learning approach for RNA-compound interaction prediction with binding site interpretability.Journal of cheminformatics · 2025Article
- 3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.Journal of computer-aided molecular design · 2025Article
- Discovery of small molecule probes targeting the 5' stem-loop in the yeast U4/U6 snRNA.bioRxiv : the preprint server for biology · 2025Article
- AI-Integrated QSAR Modeling for Enhanced Drug Discovery: From Classical Approaches to Deep Learning and Structural Insight.International journal of molecular sciences · 2025Review
- Making Sense of Heteroatom Effects in π-π Interactions.Journal of the American Chemical Society · 2025Article
- Review
- Mechanistic studies of small molecule ligands selective to RNA single G bulges.Nucleic acids research · 2025Article
- Stacking Interactions of Druglike Heterocycles with Nucleobases.Journal of chemical information and modeling · 2025Article
- Three- and four-stranded nucleic acid structures and their ligands.RSC chemical biology · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The diversity of RNA structural elements and their documented role in human diseases make RNA an attractive therapeutic target. However, progress in drug discovery and development has been hindered by challenges in the determination of high-resolution RNA structures and a limited understanding of the parameters that drive RNA recognition by small molecules, including a lack of validated quantitative structure-activity relationships (QSARs). Herein, we develop QSAR models that quantitatively predict both thermodynamic- and kinetic-based binding parameters of small molecules and the HIV-1 transactivation response (TAR) RNA model system. Small molecules bearing diverse scaffolds were screened against TAR using surface plasmon resonance. Multiple linear regression (MLR) combined with feature selection afforded robust models that allowed direct interpretation of the properties critical for both binding strength and kinetic rate constants. These models were validated with new molecules, and their accurate performance was confirmed via comparison to ensemble tree methods, supporting the general applicability of this platform.
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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.