Article in JACS Au, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.
2 · The registry
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.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
9 authors.
Martin F KöllenTUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Chair of Organic Chemistry II, Technical University of Munich, 85748 Garching bei München, Germany.ORCID https://orcid.org/0000-0003-1611-5729
Maximilian G SchuhTUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Chair of Organic Chemistry II, Technical University of Munich, 85748 Garching bei München, Germany.ORCID https://orcid.org/0009-0008-2415-8810
Robin KretschmerTUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Chair of Organic Chemistry II, Technical University of Munich, 85748 Garching bei München, Germany.ORCID https://orcid.org/0009-0009-1821-9450
Joshua HesseTUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Chair of Organic Chemistry II, Technical University of Munich, 85748 Garching bei München, Germany.ORCID https://orcid.org/0009-0004-1819-7726
Dominik SchumTUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Chair of Organic Chemistry II, Technical University of Munich, 85748 Garching bei München, Germany.ORCID https://orcid.org/0009-0001-2706-6208
Junhong ChenTUM School of Natural Sciences, Department of Chemistry, Catalysis Research Center (CRC), Chair of Inorganic and Metal-Organic Chemistry, Technical University of Munich, 85748 Garching bei München, Germany.
Annkathrin I BohneTUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Chair of Biochemistry, Technical University of Munich, 85748 Garching bei München, Germany.ORCID https://orcid.org/0009-0001-9557-663X
Dominik P HalterTUM School of Natural Sciences, Department of Chemistry, Catalysis Research Center (CRC), Chair of Inorganic and Metal-Organic Chemistry, Technical University of Munich, 85748 Garching bei München, Germany.ORCID https://orcid.org/0000-0003-0733-8955
Stephan A SieberTUM School of Natural Sciences, Department of Bioscience, Center for Functional Protein Assemblies (CPA), Chair of Organic Chemistry II, Technical University of Munich, 85748 Garching bei München, Germany.ORCID https://orcid.org/0000-0002-9400-906X
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
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
Indexed as
antibioticsautomated synthesisdeep learningde novo drug designdrug discoveryGram-negativemachine learningMRSA
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.
Generative Deep Learning Pipeline Yields Potent Gram-Negative Antibiotics. · full record | OpenQuestion