ArticleBriefings in bioinformatics2024
AptaDiff: de novo design and optimization of aptamers based on diffusion models.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Molecular Engineering of Aptamers for Glioblastoma Therapy: From Simple Antagonists to AI-Driven Approaches, a Narrative Review.International journal of molecular sciences · 2026Review
- Nucleic acid aptamers: new methods for selection, target validation, molecular diagnostics and therapeutics.Signal transduction and targeted therapy · 2026Review
- Recent Progress in Artificial Intelligence in Biosensor Development: From Bioprobe Design to Fabrication and Signal Analysis.Biosensors · 2026Review
- A Multi-Engine Consensus Docking Pipeline for RNA Aptamer Screening Against ACC Oxidase (ACO): Statistical Validation, Machine Learning Analysis, and Pilot Cross-Target Evaluation Against ACC Synthase (ACS).International journal of molecular sciences · 2026Article
- AI-powered therapeutic aptamer drug discovery: Targeting the CT-domain of CTGF for duchenne muscular dystrophy.Acta pharmaceutica Sinica. B · 2026Article
- De novo design of functional nucleic acids of aptamers.Nature computational science · 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
- RaptScore: a large language model-based algorithm for versatile aptamer evaluation.Nucleic acids research · 2026Article
- Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.Bioinformatics advances · 2026Article
- From randomness to recognition: modeling the evolution of DNA sequence information during enrichment for binding.Bioinformatics advances · 2026Article
- Aptamers and aptamer-drug conjugates as synthetic immune modulators for cancer immunotherapy.Frontiers in immunology · 2026Review
- Navigating protein-nucleic acid sequence-structure landscapes with deep learning.Current opinion in structural biology · 2025Review
- Artificial Intelligence-Driven SELEX Design of Aptamer Panels for Urinary Multi-Biomarker Detection in Prostate Cancer: A Systematic and Bibliometric Review.Biomedicines · 2025Review
- Structure-enhanced deep learning accelerates aptamer selection for small molecule families like steroids.Briefings in bioinformatics · 2025Article
- RNAtranslator: Modeling protein-conditional RNA design as sequence-to-sequence natural language translation.PLoS computational biology · 2025Article
- Designing the Future of Biosensing: Advances in Aptamer Discovery, Computational Modeling, and Diagnostic Applications.Biosensors · 2025Review
- ASSET: A Framework for Decoding Aptamer Specificity of an Enriched Library by Next-Generation Sequencing of Experimental Samples.bioRxiv : the preprint server for biology · 2025Article
- Aptamer Sequence Optimization and Its Application in Food Safety Analysis.Foods (Basel, Switzerland) · 2025Review
- MGMG: Cell Morphology-Guided Molecule Generation for Drug Discovery.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Aptamers are single-stranded nucleic acid ligands, featuring high affinity and specificity to target molecules. Traditionally they are identified from large DNA/RNA libraries using $in vitro$ methods, like Systematic Evolution of Ligands by Exponential Enrichment (SELEX). However, these libraries capture only a small fraction of theoretical sequence space, and various aptamer candidates are constrained by actual sequencing capabilities from the experiment. Addressing this, we proposed AptaDiff, the first in silico aptamer design and optimization method based on the diffusion model. Our Aptadiff can generate aptamers beyond the constraints of high-throughput sequencing data, leveraging motif-dependent latent embeddings from variational autoencoder, and can optimize aptamers by affinity-guided aptamer generation according to Bayesian optimization. Comparative evaluations revealed AptaDiff's superiority over existing aptamer generation methods in terms of quality and fidelity across four high-throughput screening data targeting distinct proteins. Moreover, surface plasmon resonance experiments were conducted to validate the binding affinity of aptamers generated through Bayesian optimization for two target proteins. The results unveiled a significant boost of $87.9\%$ and $60.2\%$ in RU values, along with a 3.6-fold and 2.4-fold decrease in KD values for the respective target proteins. Notably, the optimized aptamers demonstrated superior binding affinity compared to top experimental candidates selected through SELEX, underscoring the promising outcomes of our AptaDiff in accelerating the discovery of superior aptamers.
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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.