ArticleBioinformatics (Oxford, England)2023
Potent antibiotic design via guided search from antibacterial activity evaluations.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed.
- Artificial Intelligence and the Discovery of Antibiotics: Reinventing with Opportunities, Challenges, and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- Artificial Intelligence as a Catalyst for Antimicrobial Discovery: From Predictive Models to De Novo Design.Microorganisms · 2026Review
- Article
- Classification of Acid and Alkaline Enzymes Based on Normalized Van der Waals Volume Features.Proteomics. Clinical applications · 2025Article
- Human essential gene identification based on feature fusion and feature screening.IET systems biology · 2024Article
- Identify potential drug candidates within a high-quality compound search space.Briefings in bioinformatics · 2024Article
- iGATTLDA: Integrative graph attention and transformer-based model for predicting lncRNA-Disease associations.IET systems biology · 2024Article
- Identifying nucleotide-binding leucine-rich repeat receptor and pathogen effector pairing using transfer-learning and bilinear attention network.Bioinformatics (Oxford, England) · 2024Article
- Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization.Briefings in bioinformatics · 2024Article
- iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features.International journal of molecular sciences · 2024Article
- HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction.Journal of chemical information and modeling · 2024Article
- DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.Bioinformatics (Oxford, England) · 2024Article
- Prediction of blood-brain barrier penetrating peptides based on data augmentation with Augur.BMC biology · 2024Article
- Fuzzy kernel evidence Random Forest for identifying pseudouridine sites.Briefings in bioinformatics · 2024Article
- Article
- A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation.BMC biology · 2024Article
- FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization.Briefings in bioinformatics · 2024Article
- DisoFLAG: accurate prediction of protein intrinsic disorder and its functions using graph-based interaction protein language model.BMC biology · 2024Article
- Prediction of miRNAs and diseases association based on sparse autoencoder and MLP.Frontiers in genetics · 2024Article
- iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model.Briefings in bioinformatics · 2023Article
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
motivationThe emergence of drug-resistant bacteria makes the discovery of new antibiotics an urgent issue, but finding new molecules with the desired antibacterial activity is an extremely difficult task. To address this challenge, we established a framework, MDAGS (Molecular Design via Attribute-Guided Search), to optimize and generate potent antibiotic molecules.
resultsBy designing the antibacterial activity latent space and guiding the optimization of functional compounds based on this space, the model MDAGS can generate novel compounds with desirable antibacterial activity without the need for extensive expensive and time-consuming evaluations. Compared with existing antibiotics, candidate antibacterial compounds generated by MDAGS always possessed significantly better antibacterial activity and ensured high similarity. Furthermore, although without explicit constraints on similarity to known antibiotics, these candidate antibacterial compounds all exhibited the highest structural similarity to antibiotics of expected function in the DrugBank database query. Overall, our approach provides a viable solution to the problem of bacterial drug resistance. AVAILABILITY AND IMPLEMENTATION: Code of the model and datasets can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MDAGS). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What Socratic 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 Socratic graph.