ArticleBriefings in bioinformatics2024
Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- GDA-Pred: Generative AI-Driven Data Augmentation for Improved Prediction of IL-6 and IL-13-Inducing Peptides.International journal of molecular sciences · 2026Article
- Discovery of potent low-toxicity antimicrobial peptides through diffusion modeling.Nature communications · 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
- PlantAMP: A fine-tuned protein large language model for plant antimicrobial peptide prediction.Plant communications · 2026Article
- NeuroPpred-MSN: A Neuropeptide Prediction Model Based on Multi-feature Fusion and Siamese Networks.Interdisciplinary sciences, computational life sciences · 2026Article
- A PLUM Job: Peptide modeLs for Understanding and engineering antiMicrobial therapeutics.bioRxiv : the preprint server for biology · 2026Article
- SGAC: a graph neural network framework for imbalanced and structure-aware AMP classification.Briefings in bioinformatics · 2026Article
- ANIA: an inception-attention network for predicting minimum inhibitory concentration of antimicrobial peptides.Briefings in bioinformatics · 2026Article
- tsAMP: a strain-level antimicrobial peptide identification framework based on large language models and pathogen genomic variation.Frontiers in microbiology · 2026Article
- Harnessing Machine Learning Approaches for the Identification, Characterization, and Optimization of Novel Antimicrobial Peptides.Antibiotics (Basel, Switzerland) · 2025Review
- pCPPs-sADNN: predicting cell-penetrating peptides using self-attention based deep neural network.Scientific reports · 2025Article
- A novel generative framework for designing pathogen-targeted antimicrobial peptides with programmable physicochemical properties.PLoS computational biology · 2025Article
- Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Controllable Generation of Pathogen-Specific Antimicrobial Peptides Through Knowledge-Aware Prompt Diffusion Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- ESAE-SDA: ensemble sparse autoencoder framework for epigenomics-informed snoRNA-disease associations prediction.BMC bioinformatics · 2025Article
- A deep reinforcement learning platform for antibiotic discovery.bioRxiv : the preprint server for biology · 2025Article
- Classifier-driven generative adversarial networks for enhanced antimicrobial peptide design.Briefings in bioinformatics · 2025Article
- Computational Design of Potentially Multifunctional Antimicrobial Peptide Candidates via a Hybrid Generative Model.International journal of molecular sciences · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Antimicrobial peptides (AMPs), short peptides with diverse functions, effectively target and combat various organisms. The widespread misuse of chemical antibiotics has led to increasing microbial resistance. Due to their low drug resistance and toxicity, AMPs are considered promising substitutes for traditional antibiotics. While existing deep learning technology enhances AMP generation, it also presents certain challenges. Firstly, AMP generation overlooks the complex interdependencies among amino acids. Secondly, current models fail to integrate crucial tasks like screening, attribute prediction and iterative optimization. Consequently, we develop a integrated deep learning framework, Diff-AMP, that automates AMP generation, identification, attribute prediction and iterative optimization. We innovatively integrate kinetic diffusion and attention mechanisms into the reinforcement learning framework for efficient AMP generation. Additionally, our prediction module incorporates pre-training and transfer learning strategies for precise AMP identification and screening. We employ a convolutional neural network for multi-attribute prediction and a reinforcement learning-based iterative optimization strategy to produce diverse AMPs. This framework automates molecule generation, screening, attribute prediction and optimization, thereby advancing AMP research. We have also deployed Diff-AMP on a web server, with code, data and server details available in the Data Availability section.
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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.