Evidence map›Paper›PMID 41040195›Full record

ArticlebioRxiv : the preprint server for biology2025

A deep reinforcement learning platform for antibiotic discovery.

Hanqun Cao, Marcelo D T Torres, Jingjie Zhang, Zijun Gao, Fang Wu, Chunbin Gu, Jure Leskovec, Yejin Choi, Cesar de la Fuente-Nunez, Guangyong Chen and 1 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

11 authors.

Hanqun CaoDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Marcelo D T TorresMachine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Jingjie ZhangDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Zijun GaoDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Fang WuDepartment of Computer Science, Stanford University, Stanford, California, United States of America.ORCID 0000-0001-7240-3915
Chunbin GuDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Jure LeskovecDepartment of Computer Science, Stanford University, Stanford, California, United States of America.
Yejin ChoiDepartment of Computer Science, Stanford University, Stanford, California, United States of America.
Cesar de la Fuente-NunezMachine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
Guangyong ChenHangzhou Institute of Medicine, Chinese Academy of Sciences, Zhejiang, China.
Pheng-Ann HengDepartment of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.

Funding

Combining chemical and computational tools for predictive models of microbiome communitiesR35GM138201 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI DE LA FUENTE, CESAR · 2020 to 2024
$1.8M
NIGMS NIH HHS R35 GM138201
6 · The paper itself

Abstract

Antimicrobial resistance (AMR) is projected to cause up to 10 million deaths annually by 2050, underscoring the urgent need for new antibiotics. Here we present ApexAmphion, a deep-learning framework for

Indexed as

antibioticsantimicrobial peptidesApexAmphionartificial intelligenceDeep learningmachine learningpeptide designprotein language modelsreinforcement learning

Identifiers

PMID41040195
PMCPMC12485834

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.