ArticleACS medicinal chemistry letters2023
Quantum Annealing Designs Nonhemolytic Antimicrobial Peptides in a Discrete Latent Space.
Article in ACS medicinal chemistry letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 24 citations in OpenAlex.
- Formation of topologically associated chromatin domains using quantum annealing.Scientific reports · 2026Article
- Factorization machine with quadratic-optimization annealing for RNA inverse folding and evaluation of binary-integer encoding and nucleotide assignment.Scientific reports · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Review
- MOFormer: navigating the antimicrobial peptide design space with Pareto-based multi-objective transformer.Briefings in bioinformatics · 2025Article
- GPepT: A Foundation Language Model for Peptidomimetics Incorporating Noncanonical Amino Acids.ACS medicinal chemistry letters · 2025Article
- Computational Design of Potentially Multifunctional Antimicrobial Peptide Candidates via a Hybrid Generative Model.International journal of molecular sciences · 2025Article
- AI-Driven Antimicrobial Peptide Discovery: Mining and Generation.Accounts of chemical research · 2025Review
- Integrating biophysical modeling, quantum computing, and AI to discover plastic-binding peptides that combat microplastic pollution.PNAS nexus · 2025Article
- Extensive antibody search with whole spectrum black-box optimization.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Increasing the variety of antimicrobial peptides is crucial in meeting the global challenge of multi-drug-resistant bacterial pathogens. While several deep-learning-based peptide design pipelines are reported, they may not be optimal in data efficiency. High efficiency requires a well-compressed latent space, where optimization is likely to fail due to numerous local minima. We present a multi-objective peptide design pipeline based on a discrete latent space and D-Wave quantum annealer with the aim of solving the local minima problem. To achieve multi-objective optimization, multiple peptide properties are encoded into a score using non-dominated sorting. Our pipeline is applied to design therapeutic peptides that are antimicrobial and non-hemolytic at the same time. From 200 000 peptides designed by our pipeline, four peptides proceeded to wet-lab validation. Three of them showed high anti-microbial activity, and two are non-hemolytic. Our results demonstrate how quantum-based optimizers can be taken advantage of in real-world medical studies.
Identifiers
What OpenQuestion holds
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.