ArticlePLoS computational biology2022
RNA folding using quantum computers.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Quantum computing in bioinformatics: a systematic review mapping.Briefings in bioinformatics · 2024Pooled it
- Bridging technical innovation and computational advances in studies of RNA-protein assemblies.Nature reviews. Genetics · 2026Review
- Formation of topologically associated chromatin domains using quantum annealing.Scientific reports · 2026Article
- Exploring the Boundaries of Modern Quantum Annealers with RNA Structure Prediction.The journal of physical chemistry. B · 2026Article
- Hierarchical analysis of RNA secondary structures with pseudoknots based on sections.PLoS computational biology · 2026Article
- Generating three-dimensional genome structures with a variational quantum algorithm.Briefings in bioinformatics · 2025Article
- Quantum Osteoimmunology: A Paradigm Shift in Understanding and Influencing Bone-Immune Crosstalk.ChemMedChem · 2025Review
- Cleavage of MALAT1 RNA by 14-nt sgRNA-guided tRNase ZL.PloS one · 2025Article
- A primer for quantum computing and its applications to healthcare and biomedical research.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics.Molecular biotechnology · 2024Review
- Quantum computing on nucleic acid research: Approaching towards next-generation computing.Molecular therapy. Nucleic acids · 2023Article
- A biological sequence comparison algorithm using quantum computers.Scientific reports · 2023Article
- QuTIE: quantum optimization for target identification by enzymes.Bioinformatics advances · 2023Article
- The life and death of RNA across temperatures.Computational and structural biotechnology journal · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The 3-dimensional fold of an RNA molecule is largely determined by patterns of intramolecular hydrogen bonds between bases. Predicting the base pairing network from the sequence, also referred to as RNA secondary structure prediction or RNA folding, is a nondeterministic polynomial-time (NP)-complete computational problem. The structure of the molecule is strongly predictive of its functions and biochemical properties, and therefore the ability to accurately predict the structure is a crucial tool for biochemists. Many methods have been proposed to efficiently sample possible secondary structure patterns. Classic approaches employ dynamic programming, and recent studies have explored approaches inspired by evolutionary and machine learning algorithms. This work demonstrates leveraging quantum computing hardware to predict the secondary structure of RNA. A Hamiltonian written in the form of a Binary Quadratic Model (BQM) is derived to drive the system toward maximizing the number of consecutive base pairs while jointly maximizing the average length of the stems. A Quantum Annealer (QA) is compared to a Replica Exchange Monte Carlo (REMC) algorithm programmed with the same objective function, with the QA being shown to be highly competitive at rapidly identifying low energy solutions. The method proposed in this study was compared to three algorithms from literature and, despite its simplicity, was found to be competitive on a test set containing known structures with pseudoknots.
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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 Socratic graph.