ReviewAntibiotics (Basel, Switzerland)2022
Emerging Computational Approaches for Antimicrobial Peptide Discovery.
Review in Antibiotics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
28 citing papers in PubMed.
- From Innate Immunity to Cancer Therapy: Antimicrobial Peptides as Emerging Anticancer Agents.International journal of molecular sciences · 2026Review
- Redefining Therapies for Drug-Resistant Tuberculosis: Synergistic Effects of Antimicrobial Peptides, Nanotechnology, and Computational Design.Advanced healthcare materials · 2026Review
- Identification of Antimicrobial Peptide Variants From Lactobacillus spp. Against H. pylori-Mediated Gastric Cancer: An In-Silico Approach.The Korean journal of helicobacter and upper gastrointestinal research · 2026Article
- Aquatic-derived antimicrobial peptides and their strategically modified analogues as prospective anticancer therapeutics: a comprehensive systematic review of enhancement methodologies and mechanistic insights.Frontiers in chemistry · 2026Review
- Mapping the Antibiofilm Peptide Space with Similarity Networks and Curated Negative Sets.ACS omega · 2025Article
- Structural and mechanistic divergence in LL-37, HNP-1, and Magainin-2: An integrated computational and biophysical analysis.Current research in structural biology · 2025Article
- Tetrapeptide from microbial coculture exhibiting a unique algicidal mechanism against Alexandrium fundyense.Scientific reports · 2025Article
- Half-Space Proximal Networks (HSPNs): A Proxy for Multi-Query Similarity Searching Models Predicting Tumor-Homing Peptides.ACS omega · 2025Article
- A Comprehensive Overview of Antimicrobial Peptides: Broad-Spectrum Activity, Computational Approaches, and Applications.Antibiotics (Basel, Switzerland) · 2025Review
- Repositioning Antimicrobial Peptides Against WHO-Priority Fungi.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Analyzing the Challenges and Opportunities Associated With Harnessing New Antibiotics From the Fungal Microbiome.MicrobiologyOpen · 2025Review
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
- Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics.Molecules (Basel, Switzerland) · 2025Article
- Cuproptosis Cell Death Molecular Events and Pathways to Liver Disease.Journal of inflammation research · 2025Review
- Unveiling Encrypted Antimicrobial Peptides from Cephalopods' Salivary Glands: A Proteolysis-Driven Virtual Approach.ACS omega · 2024Article
- Enhancing Antimicrobial Peptide Activity through Modifications of Charge, Hydrophobicity, and Structure.International journal of molecular sciences · 2024Review
- Peptide hemolytic activity analysis using visual data mining of similarity-based complex networks.NPJ systems biology and applications · 2024Article
- From Data to Decisions: Leveraging Artificial Intelligence and Machine Learning in Combating Antimicrobial Resistance - a Comprehensive Review.Journal of medical systems · 2024Review
- Review
- Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics.Current pharmaceutical design · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.
Indexed as
Identifiers
What OpenQuestion holds
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.