ArticleMolecular therapy. Nucleic acids2025
DeepAptamer: Advancing high-affinity aptamer discovery with a hybrid deep learning model.
Article in Molecular therapy. Nucleic acids, 2025. 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.
- Aptamer-Based Heterobifunctional Targeted Degraders in Disease Treatment.Molecules (Basel, Switzerland) · 2026Review
- Review
- AI-powered therapeutic aptamer drug discovery: Targeting the CT-domain of CTGF for duchenne muscular dystrophy.Acta pharmaceutica Sinica. B · 2026Article
- Generative Artificial Intelligence Optimization of Albumin Binders: Coumarin and Fatty Acid Derivatives.Journal of chemical information and modeling · 2026Article
- Advancements in Nanomaterial-Based Biosensors for Neuropsychiatric and Neurodegenerative Diagnostics: From Biomarker Discovery to Clinical Translation.Biosensors · 2026Review
- Aptamers in cancer therapy: Why has clinical translation lagged behind preclinical promise?Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2026Review
- Quantum dot-DNA microsphere aptamer biosensor with AI-assisted structural modeling for rapid detection of the lung cancer biomarker USE1.Journal of nanobiotechnology · 2026Article
- AI-Assisted Molecular Biosensors: Design Strategies for Wearable and Real-Time Monitoring.International journal of molecular sciences · 2026Review
- Advances in DNAzyme Selection, Molecular Engineering and Biomedical Applications.International journal of molecular sciences · 2026Review
- Chemically modified DNA aptamers and DNAzymes for expanded functional capabilities.Chemical science · 2026Review
- Aptamer-Based Delivery of Genes and Drugs Across the Blood-Brain Barrier.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Aptamers and aptamer-drug conjugates as synthetic immune modulators for cancer immunotherapy.Frontiers in immunology · 2026Review
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.Bioinformatics advances · 2026Article
- Harnessing in Silico Design for Electrochemical Aptasensor Optimization: Detection of Okadaic Acid (OA).Biosensors · 2025Article
- Therapeutic Oligonucleotides for Neurodegenerative Diseases: Aptamer Strategies and Clay Nanoparticle-Based Delivery.Chemical record (New York, N.Y.) · 2025Review
- Review
- Designing the Future of Biosensing: Advances in Aptamer Discovery, Computational Modeling, and Diagnostic Applications.Biosensors · 2025Review
- Advancements and Prospects in DNA-Based Bioanalytical Technology for Environmental Toxicant Detection.JACS Au · 2025Review
- Recent Advances in the Development of Functional Nucleic Acid Biosensors Based on Aptamer-Rolling Circle Amplification.Molecules (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oligonucleotide aptamers are typically identified through a rigorous and time-consuming process known as systematic evolution of ligands by exponential enrichment (SELEX), which requires 20 to 30 iterative rounds to eliminate non/weak binding sequences and enrich tight binding sequences with high affinity. Moreover, inherent experimental biases and non-specific interactions within SELEX could inadvertently exclude high-affinity candidates, leading to a high failure rate. To address these challenges, we proposed DeepAptamer for identifying high-affinity sequences from unenriched early SELEX rounds. As a hybrid neural network model combining convolutional neural networks and bidirectional long short-term memory, DeepAptamer integrated sequence composition and structural features to predict aptamer binding affinities and potential binding motifs. Trained on comprehensive SELEX data, DeepAptamer outperformed existing models in accuracy as substantiated by experimental evidence. More importantly, DeepAptamer effectively identified key nucleotides for target binding. DeepAptamer can efficiently identify high-affinity aptamers against various targets, enhancing its potential to discover promising sequences in initial screening stages and obviating the 20-30 iterative selection rounds required for full enrichment of selection pools. This represented a notable leap forward in aptamer technology, with broad implications for its application across a spectrum of selection targets.
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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.