ArticleComputational and structural biotechnology journal2023
Application of a deep generative model produces novel and diverse functional peptides against microbial resistance.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- De Novo Multi-Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Enhancing ADMET property predictions using cross-aligned multimodal attention mechanisms.Molecular diversity · 2026Article
- Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Controllable Generation of Pathogen-Specific Antimicrobial Peptides Through Knowledge-Aware Prompt Diffusion Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A unified model of transient poration induced by antimicrobial peptides.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- AI-Driven Antimicrobial Peptide Discovery: Mining and Generation.Accounts of chemical research · 2025Review
- AMPGen: an evolutionary information-reserved and diffusion-driven generative model for de novo design of antimicrobial peptides.Communications biology · 2025Article
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
- Deep-Learning-Based Approaches for Rational Design of Stapled Peptides With High Antimicrobial Activity and Stability.Microbial biotechnology · 2025Article
- A conditional denoising VAE-based framework for antimicrobial peptides generation with preserving desirable properties.Bioinformatics (Oxford, England) · 2025Article
- TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation.Briefings in bioinformatics · 2024Article
- Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides.Briefings in bioinformatics · 2024Review
- Silicon versus Superbug: Assessing Machine Learning's Role in the Fight against Antimicrobial Resistance.Antibiotics (Basel, Switzerland) · 2023Review
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance could threaten millions of lives in the immediate future. Antimicrobial peptides (AMPs) are an alternative to conventional antibiotics practice against infectious diseases. Despite the potential contribution of AMPs to the antibiotic's world, their development and optimization have encountered serious challenges. Cutting-edge methods with novel and improved selectivity toward resistant targets must be established to create AMPs-driven treatments. Here, we present AMPTrans-lstm, a deep generative network-based approach for the rational design of AMPs. The AMPTrans-lstm pipeline involves pre-training, transfer learning, and module identification. The AMPTrans-lstm model has two sub-models, namely, (long short-term memory) LSTM sampler and Transformer converter, which can be connected in series to make full use of the stability of LSTM and the novelty of Transformer model. These elements could generate AMPs candidates, which can then be tailored for specific applications. By analyzing the generated sequence and trained AMPs, we prove that AMPTrans-lstm can expand the design space of the trained AMPs and produce reasonable and brand-new AMPs sequences. AMPTrans-lstm can generate functional peptides for antimicrobial resistance with good novelty and diversity, so it is an efficient AMPs design tool.
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Registered trials
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