ArticleBioinformatics advances2022
LMPred: predicting antimicrobial peptides using pre-trained language models and deep learning.
Article in Bioinformatics advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- SA-MTP: a structure-aware framework for multifunctional therapeutic peptide annotation.Briefings in bioinformatics · 2026Article
- Molecular fingerprints are strong models for peptide function prediction.Bioinformatics (Oxford, England) · 2026Article
- Biochemical-knowledge-driven machine learning pipeline for generating potent antimicrobial peptides.Briefings in bioinformatics · 2026Article
- Review
- IntelligentFrontiers in cellular and infection microbiology · 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
- AI-guided discovery and optimization of antimicrobial peptides through species-aware language model.Briefings in bioinformatics · 2025Article
- AI-Driven Antimicrobial Peptide Discovery: Mining and Generation.Accounts of chemical research · 2025Review
- MultiPep-DLCL: recognition of multifunctional therapeutic peptides through deep learning with label-sequence contrastive learning.Briefings in bioinformatics · 2025Article
- MFP-MFL: Leveraging Graph Attention and Multi-Feature Integration for Superior Multifunctional Bioactive Peptide Prediction.International journal of molecular sciences · 2025Article
- Leveraging large language models for peptide antibiotic design.Cell reports. Physical science · 2025Article
- AI Methods for Antimicrobial Peptides: Progress and Challenges.Microbial biotechnology · 2025Review
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- Innovative Strategies and Methodologies in Antimicrobial Peptide Design.Journal of functional biomaterials · 2024Review
- An ensemble deep learning model for predicting minimum inhibitory concentrations of antimicrobial peptides against pathogenic bacteria.iScience · 2024Article
- AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors.Bioinformatics (Oxford, England) · 2024Article
- PyAMPA: a high-throughput prediction and optimization tool for antimicrobial peptides.mSystems · 2024Article
- PLMACPred prediction of anticancer peptides based on protein language model and wavelet denoising transformation.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Antimicrobial peptides (AMPs) are increasingly being used in the development of new therapeutic drugs in areas such as cancer therapy and hypertension. Additionally, they are seen as an alternative to antibiotics due to the increasing occurrence of bacterial resistance. Wet-laboratory experimental identification, however, is both time-consuming and costly, so Results: This paper proposes a novel approach for creating model inputs; using pre-trained language models to produce contextualized embeddings, representing the amino acids within each peptide sequence, before a convolutional neural network is trained as the classifier. The results were validated on two datasets-one previously used in AMP prediction research, and a larger independent dataset created by this paper. Predictive accuracies of 93.33% and 88.26% were achieved, respectively, outperforming previous state-of-the-art classification models. Availability and implementation: All codes are available and can be accessed here: https://github.com/williamdee1/LMPred_AMP_Prediction. Supplementary information: Supplementary data are available at
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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.