ArticleCommunications biology2025
AMPGen: an evolutionary information-reserved and diffusion-driven generative model for de novo design of antimicrobial peptides.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Next-Generation Antimicrobial Peptides for Biofilm-Associated Infections: Engineering, Biomaterial Delivery and AI-Assisted Discovery.Antibiotics (Basel, Switzerland) · 2026Review
- A unified framework for potency-oriented AMP discovery via multi-modal learning and guided sequence synthesis.PLoS computational biology · 2026Article
- GDA-Pred: Generative AI-Driven Data Augmentation for Improved Prediction of IL-6 and IL-13-Inducing Peptides.International journal of molecular sciences · 2026Article
- Contemporary data-driven innovations in peptide-based therapeutic design.Briefings in bioinformatics · 2026Review
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- AI-Driven Discovery and Design of Antimicrobial Peptides: Progress, Challenges, and Opportunities.Probiotics and antimicrobial proteins · 2026Review
- Artificial Intelligence-Driven Discovery and Optimization of Antimicrobial Peptides Targeting ESKAPE Pathogens and Multidrug-Resistant Fungi.Microorganisms · 2026Review
- The rise and future of peptide-based antimicrobials.Journal of microbiology (Seoul, Korea) · 2026Review
- Peptide-based drug design using generative AI.Chemical communications (Cambridge, England) · 2026Review
- ANIA: an inception-attention network for predicting minimum inhibitory concentration of antimicrobial peptides.Briefings in bioinformatics · 2026Article
- IntelligentFrontiers in cellular and infection microbiology · 2026Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- Agrochemical innovation for crop health: moving forward through dynamic disorder.Frontiers in plant science · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid advancement of artificial intelligence (AI) has enabled de novo design of functional proteins, circumventing the reliance on natural templates or sequencing databases. However, current protein design models are ineffective in generating proteins without stable structures, such as antimicrobial peptides (AMPs), which are short and structurally flexible yet play critical biological roles. To address this challenge, we present AMPGen, an evolutionary information-reserved and diffusion-driven generative model for de novo design of target-specific AMPs. AMPGen innovates AI tools, including a generator, a discriminator, and a scorer, along with biochemical knowledge-based screening programs. The generator employs a pre-trained, order-agnostic autoregressive diffusion model, which performs axial attention to capture protein evolutionary information from multiple sequence alignments (MSAs). The AMP-MSA conditional input raises the success rate of generated AMPs, which are subsequently filtered based on physicochemical properties and assessed by an XGBoost-based discriminator. The final target-specific scoring is performed with an LSTM-based scorer, resulting in high-quality AMP candidates. In this study, of the 40 de novo designed AMP candidates for verification, 38 were successfully synthesized, and among them, 81.58% demonstrated antibacterial activity. These AMPs designed by AMPGen are absent from existing AMP databases, and exhibit high antibacterial capacity, sequence diversity, and broad-spectrum activity.
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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.