ArticleBMC bioinformatics2023
AptaTrans: a deep neural network for predicting aptamer-protein interaction using pretrained encoders.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Nucleic acid aptamers: new methods for selection, target validation, molecular diagnostics and therapeutics.Signal transduction and targeted therapy · 2026Review
- circMAC: microRNA-conditioned binding-site localization on circular RNA isoforms.Briefings in bioinformatics · 2026Article
- Recent Progress in Artificial Intelligence in Biosensor Development: From Bioprobe Design to Fabrication and Signal Analysis.Biosensors · 2026Review
- AI-powered therapeutic aptamer drug discovery: Targeting the CT-domain of CTGF for duchenne muscular dystrophy.Acta pharmaceutica Sinica. B · 2026Article
- Quantum dot-DNA microsphere aptamer biosensor with AI-assisted structural modeling for rapid detection of the lung cancer biomarker USE1.Journal of nanobiotechnology · 2026Article
- In silico aptamer design: from sequence selection to structural optimization and computational modelling strategies.Journal of computer-aided molecular design · 2026Review
- Novel Homo-SELEX for selecting universal aptamer for cross-species kidney injury biomarker KIM-1 in muti-scenario situ imaging.Journal of nanobiotechnology · 2026Article
- Single-round evolution of RNA aptamers with GRAPE-LM.Nature biotechnology · 2026Article
- Aptamer-Based Delivery of Genes and Drugs Across the Blood-Brain Barrier.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Aptamer Engineering: Strategies for Discovering Functional Nucleic Acids for Next-Generation Diagnostics and Biosensing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.Bioinformatics advances · 2026Article
- Structure-enhanced deep learning accelerates aptamer selection for small molecule families like steroids.Briefings in bioinformatics · 2025Article
- Therapeutic Oligonucleotides for Neurodegenerative Diseases: Aptamer Strategies and Clay Nanoparticle-Based Delivery.Chemical record (New York, N.Y.) · 2025Review
- Designing the Future of Biosensing: Advances in Aptamer Discovery, Computational Modeling, and Diagnostic Applications.Biosensors · 2025Review
- Progress in Aptamer Research and Future Applications.ChemistryOpen · 2025Review
- Using aptamers for targeted delivery of RNA therapies.Molecular therapy : the journal of the American Society of Gene Therapy · 2025Review
- Matrix-based vector representations in neural networks for classifying molecular biology data.Bioinformatics advances · 2025Article
- AI-driven biomaterial design: an intelligent closed loop from reverse design to biological response.Frontiers in cell and developmental biology · 2025Review
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAptamers, which are biomaterials comprised of single-stranded DNA/RNA that form tertiary structures, have significant potential as next-generation materials, particularly for drug discovery. The systematic evolution of ligands by exponential enrichment (SELEX) method is a critical in vitro technique employed to identify aptamers that bind specifically to target proteins. While advanced SELEX-based methods such as Cell- and HT-SELEX are available, they often encounter issues such as extended time consumption and suboptimal accuracy. Several In silico aptamer discovery methods have been proposed to address these challenges. These methods are specifically designed to predict aptamer-protein interaction (API) using benchmark datasets. However, these methods often fail to consider the physicochemical interactions between aptamers and proteins within tertiary structures.
resultsIn this study, we propose AptaTrans, a pipeline for predicting API using deep learning techniques. AptaTrans uses transformer-based encoders to handle aptamer and protein sequences at the monomer level. Furthermore, pretrained encoders are utilized for the structural representation. After validation with a benchmark dataset, AptaTrans has been integrated into a comprehensive toolset. This pipeline synergistically combines with Apta-MCTS, a generative algorithm for recommending aptamer candidates.
conclusionThe results show that AptaTrans outperforms existing models for predicting API, and the efficacy of the AptaTrans pipeline has been confirmed through various experimental tools. We expect AptaTrans will enhance the cost-effectiveness and efficiency of SELEX in drug discovery. The source code and benchmark dataset for AptaTrans are available at https://github.com/pnumlb/AptaTrans .
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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.