ArticleNature biomedical engineering2021
Accelerated antimicrobial discovery via deep generative models and molecular dynamics simulations.
Article in Nature biomedical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 190 papers, 3 of them syntheses that pooled it.
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
190 citing papers in PubMed, 3 syntheses or guidelines pooled it, 366 citations in OpenAlex.
- Deep Generative Models for the Discovery of Antiviral Peptides Targeting Dengue Virus: A Systematic Review.International journal of molecular sciences · 2025Pooled it
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation.International journal of molecular sciences · 2023Pooled it
- Artificial intelligence catalyzes antimicrobial peptide design.Synthetic and systems biotechnology · 2027Review
- Engineering short-sequence elements for condensate-like assemblies by de novo design.Synthetic and systems biotechnology · 2027Article
- Spatially Confined/Ligand-Directed Ultrabright NIR-AIE Nanoprobe for In Vitro Rapid Detection and In Situ Imaging of Bacterial Septic Arthritis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI for bioactive materials: From material design to biological applications.Bioactive materials · 2026Review
- Animal-Derived Antimicrobial Peptides: Emerging Therapeutic Strategies against Multidrug-Resistant Pathogens.Probiotics and antimicrobial proteins · 2026Review
- Lipidomic Profile Reconstruction of Therapeutic Membrane Targets Using Physics-Based Optimization with Limited Activity Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence driven diet-intestinal microbiota-host health integration: A four-dimensional paradigm for advancing host wellness research.iMetaOmics · 2026Review
- A unified framework for potency-oriented AMP discovery via multi-modal learning and guided sequence synthesis.PLoS computational biology · 2026Article
- Bridging linguistic reasoning and biophysical reality toward peptide engineering via instruction-tuned language modeling.Bioinformatics (Oxford, England) · 2026Article
- GANs in Peptide Drug Discovery: From De Novo Design to Multi-Property Optimization and Clinical Translation.Probiotics and antimicrobial proteins · 2026Review
- Assembly-formed bioorthogonal chimeric artificial receptors enable high-contrast fluorescence imaging of tumors.Science advances · 2026Article
- Reimagining antimicrobial resistance: AI-driven predictive epidemiology and the C-AMRE framework for next-generation antibiotic discovery.The Journal of antibiotics · 2026Review
- Targeted Design of Novel Antimicrobial Peptides againstACS omega · 2026Article
- Antibacterial drug discovery: challenges and preclinical promises from synthetic small molecules.Chemical Society reviews · 2026Review
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- DeepACPred: an integrated multistage framework for anticancer peptide discovery and activity prediction.Briefings in bioinformatics · 2026Article
- A Comprehensive Review of Antimicrobial Peptides and Smart Biomaterials in Chronic Wound Therapy: Overcoming Biofilms, Resistance, and Translational Barriers.International journal of molecular sciences · 2026Review
130 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
15 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The de novo design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency against diverse Gram-positive and Gram-negative pathogens (including multidrug-resistant Klebsiella pneumoniae) and a low propensity to induce drug resistance in Escherichia coli. Both peptides have low toxicity, as validated in vitro and in mice. We also show using live-cell confocal imaging that the bactericidal mode of action of the peptides involves the formation of membrane pores. The combination of deep learning and molecular dynamics may accelerate the discovery of potent and selective broad-spectrum antimicrobials.
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.