ArticleScientific reports2025
Prediction of inhibitory peptides against E.coli with desired MIC value.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Rationally Engineered D-Amino Acid Peptide DT7-3 Combats Multidrug-ResistantMicroorganisms · 2026Article
- Review
- Antimicrobial Peptides Against Antimicrobial-Resistant Bacteria: Focus on Machine Learning.Infection and drug resistance · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In the past, several methods have been developed for predicting antibacterial and antimicrobial peptides, but only limited attempts have been made to predict their minimum inhibitory concentration (MIC) values. In this study, we developed predictive models for MIC values of antibacterial peptides against Escherichia coli (E. coli), comprised of 3143 peptides for training and 786 peptides for validation, with experimentally determined MIC values. We found that the Composition Enhanced Transition and Distribution (CeTD) attributes significantly correlate with MIC values. Initially, we attempted to estimate MIC using BLAST similarity searches but found them inadequate. Subsequently, we employed machine learning regression models that integrated various features, including peptide composition, binary profiles and embeddings from large language models. Feature selection techniques, particularly mRMR, were utilized to refine our model inputs. Our Random Forest regressor built using default parameters achieved a correlation coefficient (R) of 0.78, R
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.