PXD066005-1

PXD066005 is an original dataset announced via ProteomeXchange.

Title	Generative Deep Learning Pipeline Yields Potent Gram-negative Antibiotics
Description	The escalating crisis of multiresistant bacteria demands the rapid discovery of novel antibiotics that transcend the limitations imposed by the biased chemical space of current libraries. To address this challenge, we introduce an innovative deep learning-driven pipeline for de novo antibiotic design. This unique approach leverages a chemical language model, trained on a diverse chemical space encompassing drug-like molecules and natural products, coupled with transfer learning on diverse antibiotic scaffolds to efficiently generate structurally unprecedented antibiotic candidates. Through the use of predictive modeling and expert curation, we prioritized and synthesized the most promising and readily available candidates. Notably, our efforts culminated in a lead candidate demonstrating potent activity against methicillin-resistant Staphylococcus aureus. Iterative refinement through automated synthesis of 40 derivatives yielded a suite of active compounds, including 30 with activity against S. aureus and 17 against Escherichia coli. Among these, lead compound D8 exhibited remarkable submicromolar and single-digit micromolar potency against the aforementioned pathogens, respectively. Mechanistic investigations point to the generation of radical species as its primary mode of action. This work showcases the power of our innovative deep learning framework to significantly accelerate and expand the horizons of antibiotic drug discovery.
HostingRepository	PRIDE
AnnounceDate	2025-10-06
AnnouncementXML	Submission_2025-10-05_18:26:21.310.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Joshua Hesse
SpeciesList	scientific name: Escherichia coli; NCBI TaxID: NEWT:562;
ModificationList	No PTMs are included in the dataset
Instrument	timsTOF Pro

Revision	Datetime	Status	ChangeLog Entry
0	2025-07-10 05:45:09	ID requested
⏵ 1	2025-10-05 18:26:22	announced

K, ö, llen MF, Schuh MG, Kretschmer R, Hesse J, Schum D, Chen J, Bohne AI, Halter DP, Sieber SA, Generative Deep Learning Pipeline Yields Potent Gram-Negative Antibiotics. JACS Au, 5(9):4249-4259(2025) [pubmed]

10.1021/jacsau.5c00602;

submitter keyword: machine learning,deep learning, antibiotics, drug discovery, MRSA, automated synthesis, de novo drug design, Gram-negative

Stephan Axel Sieber
contact affiliation	TUM School of Natural Sciences, Department Biosciences, Chair of Organic Chemistry II, Center for Functional Protein Assemblies (CPA), Technical University Munich (TUM), Ernst-Otto-Fischer Str. 8, Garching, 85748, Germany
contact email	stephan.sieber@tum.de
lab head
Joshua Hesse
contact affiliation	Technical University of Munich, Chair of Organic Chemistry II
contact email	joshua.hesse@tum.de
dataset submitter

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://ftp.pride.ebi.ac.uk/pride/data/archive/2025/10/PXD066005

PRIDE project URI

• PRIDE
  1. PXD066005
     1. Label: PRIDE project
     2. Name: Generative Deep Learning Pipeline Yields Potent Gram-negative Antibiotics