PXD067530

PXD067530 is an original dataset announced via ProteomeXchange.

Title	Deep learning-enabled discovery of antibiotics effective against Neisseria gonorrhoeae
Description	Neisseria gonorrhoeae is a Gram-negative, sexually transmitted pathogen that poses a major public health threat due to rapidly increasing resistance to all recommended antibiotics. Addressing this crisis requires more efficient approaches to antibiotic discovery and the replenishment of the dwindling drug development pipeline. Here, we demonstrate that deep learning models can augment high-throughput screening to identify readily available molecules with narrow-spectrum activity against multidrug-resistant N. gonorrhoeae. We phenotypically tested 38,650 small molecules for growth inhibition and used these data to train a predictive graph neural network (GNN). Benchmarking against alternative architectures, including large language models, revealed that GNNs most effectively identified active, drug-like molecules that were structurally distinct from both the training set and known antibiotics. Applying the model to ~6 million compounds in silico, we prioritized 213 for experimental testing and found that 83 (38%) inhibited N. gonorrhoeae growth. Two compounds were structurally novel, potent against all tested multidrug-resistant strains, displayed favorable selectivity indices, and were rapidly bactericidal with low frequencies of resistance. Multi-omics analyses revealed that these compounds circumvent resistance by targeting previously unexploited pathways in N. gonorrhoeae. Our findings establish a paradigm for deep learning-enabled discovery of selective antibacterial agents and provide a promising path toward addressing the urgent threat of antimicrobial resistance in N. gonorrhoeae
HostingRepository	MassIVE
AnnounceDate	2026-05-05
AnnouncementXML	Submission_2026-05-05_22:50:39.708.xml
DigitalObjectIdentifier
ReviewLevel	Non peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	danilo ritz
SpeciesList	scientific name: Neisseria gonorrhoeae; NCBI TaxID: 485;
ModificationList	No PTMs are included in the dataset
Instrument	Orbitrap Fusion Lumos

Revision	Datetime	Status	ChangeLog Entry
0	2025-08-20 23:29:28	ID requested
⏵ 1	2026-05-05 22:50:40	announced

no publication

submitter keyword: Deep learning, Infectious disease, antibiotics, drug discovery, Neisseria gonorrhoeae, DatasetType:Proteomics

Alexander Schmidt
contact affiliation	Biozentrum, Universtiy of Basel, 4056 Basel, Switzerland
contact email	alex.schmidt@unibas.ch
lab head
danilo ritz
contact affiliation	University of Basel
contact email	danilo.ritz@unibas.ch
dataset submitter

MassIVE dataset URI

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://massive-ftp.ucsd.edu/v10/MSV000098897/