ArticleNucleic acids research2018
aBiofilm: a resource of anti-biofilm agents and their potential implications in targeting antibiotic drug resistance.
Article in Nucleic acids research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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Who cites it
81 citing papers in PubMed, 185 citations in OpenAlex.
- Bioprospecting of cultivable actinobacteria from tropical peat swamp forest soil reveals Streptomyces with potent antimicrobial activity.Antonie van Leeuwenhoek · 2026Article
- A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.BMC bioinformatics · 2026Article
- Preclinical Pharmacokinetics and Pharmacodynamics of Antibiotic Adjuvants.ACS infectious diseases · 2026Review
- Prevalence of quorum sensing luxS gene in multidrug resistant uropathogens.Molecular biology reports · 2026Article
- PecbloodinApplied and environmental microbiology · 2026Article
- A microbe-drug association prediction model based on graph attention networks and rotation forest.Frontiers in bioinformatics · 2026Article
- Effects of Fermentation Period on Metabolite Production and Bioactivity in Two Sponge-Associated Bacteria.Scientifica · 2026Article
- MetaMDA: explainable prediction of microbe-drug association utilizing random walks on a microbe-metabolite-drug heterogeneous network.Bioinformatics (Oxford, England) · 2025Article
- HAFMMDA: HIN2vec-Based Attentional Factorization Machines for Predicting Microbe-Drug Associations.Interdisciplinary sciences, computational life sciences · 2025Article
- Structure-sensitive transformer and multi-view graph contrastive learning enhanced prediction of drug-related microbes.BMC bioinformatics · 2025Article
- KGCLMDA: a computational model for predicting latent associations of microbial drugs using knowledge graphs and contrastive learning.Bioinformatics (Oxford, England) · 2025Article
- BioWalk-MDA: a novel approach for large-scale predicting metabolite-drug associations based on multi layered biomedical knowledge graphs.Briefings in bioinformatics · 2025Article
- Next-generation antifungal drugs: Mechanisms, efficacy, and clinical prospects.Acta pharmaceutica Sinica. B · 2025Review
- AEPMA: peptide-microbe association prediction based on autoevolutionary heterogeneous graph learning.Briefings in bioinformatics · 2025Article
- NRGCNMDA: Microbe-Drug Association Prediction Based on Residual Graph Convolutional Networks and Conditional Random Fields.Interdisciplinary sciences, computational life sciences · 2025Article
- Efficacy of avocado seed extract in preventing, inhibiting, and eliminatingVeterinary world · 2025Article
- Structural-based Study to Identify the Repurposed Candidates against Bacterial Infections.Current medicinal chemistry · 2025Article
- DHCLHAM: microbe-drug interaction prediction based on dual-hypergraph contrastive learning framework with hierarchical attention mechanism.Frontiers in microbiology · 2025Article
- Article
- Anti-EBV: Artificial intelligence driven predictive modeling for repurposing drugs as potential antivirals against Epstein-Barr virus.Computational and structural biotechnology journal · 2025Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biofilms play an important role in the antibiotic drug resistance, which is threatening public health globally. Almost, all microbes mimic multicellular lifestyle to form biofilm by undergoing phenotypic changes to adapt adverse environmental conditions. Many anti-biofilm agents have been experimentally validated to disrupt the biofilms during last three decades. To organize this data, we developed the 'aBiofilm' resource (http://bioinfo.imtech.res.in/manojk/abiofilm/) that harbors a database, a predictor, and the data visualization modules. The database contains biological, chemical, and structural details of 5027 anti-biofilm agents (1720 unique) reported from 1988-2017. These agents target over 140 organisms including Gram-negative, Gram-positive bacteria, and fungus. They are mainly chemicals, peptides, phages, secondary metabolites, antibodies, nanoparticles and extracts. They show the diverse mode of actions by attacking mainly signaling molecules, biofilm matrix, genes, extracellular polymeric substances, and many more. The QSAR based predictor identifies the anti-biofilm potential of an unknown chemical with an accuracy of ∼80.00%. The data visualization section summarized the biofilm stages targeted (Circos plot); interaction maps (Cytoscape) and chemicals diversification (CheS-Mapper) of the agents. This comprehensive platform would help the researchers to understand the multilevel communication in the microbial consortium. It may aid in developing anti-biofilm therapeutics to deal with antibiotic drug resistance menace.
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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.