ArticleScientific reports2025
A machine learning method for predicting molecular antimicrobial activity.
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 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Artificial Intelligence in Pharmaceutical Care:Saudi medical journal · 2026Review
- Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities.JAC-antimicrobial resistance · 2026Review
- Improving Generalizability in Whole-Cell Antibiotic Discovery Through Active Learning.bioRxiv : the preprint server for biology · 2026Article
- Livestock immunomodulators as alternatives to antimicrobials - a roadmap for the fight against drug resistance.BMC veterinary research · 2026Review
- Chemical space visualization at scale: a survey of end-to-end pipelines and dataset-size archetypes.Journal of cheminformatics · 2026Review
- Pharmacokinetic Prediction of Repurposed Drugs for PDAC Using Artificial Intelligence.ACS omega · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Comprehensive genomics, systems, and structural assessment for novel target identification in penicillin-resistantFrontiers in bioinformatics · 2026Article
- Review
- The Gut Microbiome and Colistin Resistance: A Hidden Driver of Antimicrobial Failure.International journal of molecular sciences · 2025Review
- Novel Antibacterial Approaches and Therapeutic Strategies.Antibiotics (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
In response to the increasing concern over antibiotic resistance and the limitations of traditional methods in antibiotic discovery, we introduce a machine learning-based method named MFAGCN. This method predicts the antimicrobial efficacy of molecules by integrating three types of molecular fingerprints-MACCS, PubChem, and ECFP-along with molecular graph representations as input features, with a specific focus on molecular functional groups. MFAGCN incorporates an attention mechanism to assign different weights to the importance of information from different neighboring nodes. Comparative experiments with baseline models on two public datasets demonstrate MFAGCN's superior performance. Additionally, we conducted an analysis of the functional group distribution in both the training and test sets to validate the model's predictions. Furthermore, structural similarity analyses with known antibiotics are performed to prevent the rediscovery of established antibiotics. This approach enables researchers to rapidly screen molecules with potent antimicrobial properties and facilitates the identification of functional groups that influence antimicrobial performance, providing valuable insights for further antibiotic development.
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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.