ArticleNature communications2021
Machine learning guided aptamer refinement and discovery.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 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
69 citing papers in PubMed.
- Nucleic acid aptamers: new methods for selection, target validation, molecular diagnostics and therapeutics.Signal transduction and targeted therapy · 2026Review
- Review
- Innovations in Aptamer Technology: SELEX To Intelligent Molecular Engineering and Clinical Translation.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Review
- Recent Progress in Artificial Intelligence in Biosensor Development: From Bioprobe Design to Fabrication and Signal Analysis.Biosensors · 2026Review
- Multivalent Aptamers: Contemporary Engineering Strategies and Biomedical Applications.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- AI-powered therapeutic aptamer drug discovery: Targeting the CT-domain of CTGF for duchenne muscular dystrophy.Acta pharmaceutica Sinica. B · 2026Article
- Stability modification of therapeutic aptamers: from biostability bottlenecks to nuclease-resistant construct design.RSC chemical biology · 2026Review
- Probing the Specificity of Fluorescent Deoxyribozymes Using Single-Step Selections and Machine Learning.ACS chemical biology · 2026Article
- Are we there yet with XNA aptamers?RSC advances · 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
- RaptScore: a large language model-based algorithm for versatile aptamer evaluation.Nucleic acids research · 2026Article
- Aptamer-Engineered Ellipsometry for Clinical Detection of BALF-Derived Exosomes: Multi-Level Engineering for Prognostic Evaluation of Immunotherapy Responses.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Aptamer Engineering: Strategies for Discovering Functional Nucleic Acids for Next-Generation Diagnostics and Biosensing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Aptamers and aptamer-drug conjugates as synthetic immune modulators for cancer immunotherapy.Frontiers in immunology · 2026Review
- Using structured libraries, selection, and machine learning to rapidly explore the sequence space of a fluorescent deoxyribozyme.Nucleic acids research · 2025Article
- Design and Characterization of Aptamers to Antibiotic Kanamycin with Improved Affinity.International journal of molecular sciences · 2025Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, are practically constrained to a fraction of the theoretical sequence space. Machine learning provides an opportunity to intelligently navigate this space to identify high-performing aptamers. Here, we propose an approach that employs particle display (PD) to partition a library of aptamers by affinity, and uses such data to train machine learning models to predict affinity in silico. Our model predicted high-affinity DNA aptamers from experimental candidates at a rate 11-fold higher than random perturbation and generated novel, high-affinity aptamers at a greater rate than observed by PD alone. Our approach also facilitated the design of truncated aptamers 70% shorter and with higher binding affinity (1.5 nM) than the best experimental candidate. This work demonstrates how combining machine learning and physical approaches can be used to expedite the discovery of better diagnostic and therapeutic agents.
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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.