ArticleJournal of chemical information and modeling2023
Serverless Prediction of Peptide Properties with Recurrent Neural Networks.
Article in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed.
- De novo generation and computational screening of dual-targeting short peptide inhibitors against PBP2b and PBP2x in drug-resistant Streptococcus Pneumoniae.Molecular diversity · 2026Article
- PeptiVerse: A unified platform for therapeutic peptide property prediction.Nature communications · 2026Article
- Leishmanicidal Action of the Peptides 19-4LF, 19-2.5 and 19-2.5LF Topically Administered on Cutaneous Lesions Caused byPharmaceutics · 2026Article
- Subtimizer: Computational Workflow for Structure-Guided Design of Potent and Selective Kinase Peptide Substrates.Journal of chemical information and modeling · 2026Article
- PepFoundry: A Pipeline for Building Machine-Learning Ready Representations of Nonstandard Peptides Containing Cycles, Non-natural Residues, Polymer Units, and More.Journal of chemical information and modeling · 2026Article
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- PeptiVerse: A Unified Platform for Therapeutic Peptide Property Prediction.bioRxiv : the preprint server for biology · 2026Article
- Lab to codes: multimodel advanced AI-driven pipelines to drive antimicrobial peptide design for the post-antibiotic era.Frontiers in cellular and infection microbiology · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Spider Venom-Derived Peptide Exhibits Dual Anti-Inflammatory and Antioxidative Activities in LPS-Stimulated BEAS-2B Cells.Antioxidants (Basel, Switzerland) · 2025Article
- A large language model for predicting neurotoxic peptides and neurotoxins.Protein science : a publication of the Protein Society · 2025Article
- AI-Driven Antimicrobial Peptide Discovery: Mining and Generation.Accounts of chemical research · 2025Review
- From Neural Networks to Transformers: Achieving High and Fast Precision in Fluorescence Correlation Spectroscopy Analysis.ACS omega · 2025Article
- Prediction of hemolytic peptides and their hemolytic concentration.Communications biology · 2025Article
- PepFuNN: Novo Nordisk Open-Source Toolkit to Enable Peptide in Silico Analysis.Journal of peptide science : an official publication of the European Peptide Society · 2025Article
- Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide Properties.Journal of chemical information and modeling · 2025Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- AbAMPdb: a database of Acinetobacter baumannii specific antimicrobial peptides.Database : the journal of biological databases and curation · 2024Article
- Generative β-hairpin design using a residue-based physicochemical property landscape.Biophysical journal · 2024Article
- Can large language models predict antimicrobial peptide activity and toxicity?RSC medicinal chemistry · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
We present three deep learning sequence-based prediction models for peptide properties including hemolysis, solubility, and resistance to nonspecific interactions that achieve comparable results to the state-of-the-art models. Our sequence-based solubility predictor, MahLooL, outperforms the current state-of-the-art methods for short peptides. These models are implemented as a static website without the use of a dedicated server or cloud computing. Web-based models like this allow for accessible and effective reproducibility. Most existing approaches rely on third-party servers that typically require upkeep and maintenance. Our predictive models do not require servers, require no installation of dependencies, and work across a range of devices. The specific architecture is bidirectional recurrent neural networks. This
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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.