ArticleChemical biology & drug design2015
Machine-learning techniques applied to antibacterial drug discovery.
Article in Chemical biology & drug design, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed, 75 citations in OpenAlex.
- Beyond the flat screen: evaluating virtual reality as a complementary tool for molecular docking in drug discovery.Journal of computer-aided molecular design · 2026Review
- Antibacterial Drug Discovery: Deep Learning Successes and Challenges through the Structural Biology Lens.Computational and structural biotechnology journal · 2026Review
- Machine Learning-Guided Design of Rhenium Tricarbonyl Complexes for Next-Generation Antibiotics.ACS bio & med chem Au · 2025Article
- AI-enhanced bioprocess technologies: machine learning implementations from upstream to downstream operations.World journal of microbiology & biotechnology · 2025Review
- Article
- Predicting Treatment Outcomes in Patients with Drug-Resistant Tuberculosis and Human Immunodeficiency Virus Coinfection, Using Supervised Machine Learning Algorithm.Pathogens (Basel, Switzerland) · 2024Article
- Machine Learning-Based Virtual Screening of Antibacterial Agents against Methicillin-Susceptible and ResistantJournal of chemical information and modeling · 2024Article
- Combined substituent number utilized machine learning for the development of antimicrobial agent.Scientific reports · 2024Article
- Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions.Journal of cheminformatics · 2024Article
- Using display technologies to identify macrocyclic peptide antibiotics.Biochimica et biophysica acta. Molecular cell research · 2023Review
- Genome-scale metabolic modeling in antimicrobial pharmacology.Engineering microbiology · 2022Review
- Article
- Machine Learning in Antibacterial Drug Design.Frontiers in pharmacology · 2022Review
- A review: antimicrobial resistance data mining models and prediction methods study for pathogenic bacteria.The Journal of antibiotics · 2021Review
- Mining for novel antibiotics.Current opinion in microbiology · 2021Review
- Accelerating antibiotic discovery through artificial intelligence.Communications biology · 2021Review
- A Machine Learning Tool to Predict the Antibacterial Capacity of Nanoparticles.Nanomaterials (Basel, Switzerland) · 2021Article
- In Vitro Activity of a Novel Siderophore-Cephalosporin LCB10-0200 (GT-1), and LCB10-0200/Avibactam, against Carbapenem-ResistantPharmaceuticals (Basel, Switzerland) · 2021Article
- Towards the sustainable discovery and development of new antibiotics.Nature reviews. Chemistry · 2021Review
- Advances in the study of drug metabolism - symposium report of the 12th Meeting of the International Society for the Study of Xenobiotics (ISSX).Drug metabolism reviews · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field.
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.