ArticleNAR genomics and bioinformatics2021
Structure-based deep learning for binding site detection in nucleic acid macromolecules.
Article in NAR genomics and bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 27 citations in OpenAlex.
- In silico designing of small molecules for targeting RNA: current landscape and future directions.Journal of computer-aided molecular design · 2026Review
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Multivalent ion binding site identification with structure-based deep learning.Communications biology · 2026Article
- Deep learning insights into β-lactamase dynamics and resistance evolution.The Biochemical journal · 2026Review
- DeepRNA-DTI: a deep learning approach for RNA-compound interaction prediction with binding site interpretability.Journal of cheminformatics · 2025Article
- Biomolecular Interaction Prediction: The Era of AI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Predicting the Anti-SARS-CoV-2 Potential of Isoquinoline Alkaloids from Brazilian Siparunaceae Species Using Chemometric Tools.International journal of molecular sciences · 2025Article
- Exploring chemical space for "druglike" small molecules in the age of AI.Frontiers in molecular biosciences · 2025Review
- Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN.Nature communications · 2024Article
- Advances in machine-learning approaches to RNA-targeted drug design.Artificial intelligence chemistry · 2024Article
- Review
- Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2.Computational and structural biotechnology journal · 2022Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structure-based drug design (SBDD) targeting nucleic acid macromolecules, particularly RNA, is a gaining momentum research direction that already resulted in several FDA-approved compounds. Similar to proteins, one of the critical components in SBDD for RNA is the correct identification of the binding sites for putative drug candidates. RNAs share a common structural organization that, together with the dynamic nature of these molecules, makes it challenging to recognize binding sites for small molecules. Moreover, there is a need for structure-based approaches, as sequence information only does not consider conformation plasticity of nucleic acid macromolecules. Deep learning holds a great promise to resolve binding site detection problem, but requires a large amount of structural data, which is very limited for nucleic acids, compared to proteins. In this study we composed a set of ∼2000 nucleic acid-small molecule structures comprising ∼2500 binding sites, which is ∼40-times larger than previously used one, and demonstrated the first structure-based deep learning approach, BiteNet
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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.