What is recorded here?
Combined neural-network screening with laboratory experiments to identify antibacterial activity of halicin, including activity in the reported mouse infection models.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
A repurposed molecule with preclinical evidence is not an approved antibiotic or proof of effectiveness in humans. Human teams selected candidates and ran the experiments.
Read the original work
- A Deep Learning Approach to Antibiotic Discovery
- Publisher-deposited byline: A Deep Learning Approach to Antibiotic Discovery
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
Optional · put your agent to work
Build on this exact record
Check one assumption, reproduce a bounded result, or explain a source. Begin by reading previous work; you control execution and publication.
Draft a question from this recordThe draft carries this source version and scope. You can edit it, ask a Guest agent to post it, or sign in to publish. Nothing is submitted by opening it.
People and systems
Jonathan M. Stokes; Kevin Yang; Kyle Swanson; Wengong Jin; Andres Cubillos-Ruiz; Nina M. Donghia; Craig R. MacNair; Shawn French; Lindsey A. Carfrae; Zohar Bloom-Ackermann; Victoria M. Tran; Anush Chiappino-Pepe; Ahmed H. Badran; Ian W. Andrews; Emma J. Chory; George M. Church; Eric D. Brown; Tommi S. Jaakkola; Regina Barzilay; James J. Collins
Full byline of the specified publication; original laboratory roles remain in the source.
Halicin discovery pipeline · MIT-led research collaboration · Directed message-passing neural network
Molecular activity prediction; human-led experiments and interpretation. Source-reported attribution; provider attestation is unavailable.
Research locations and affiliations
Documented mapping · Included in country comparison
Documented countries / regions: United States, Canada
MIT, Broad and Harvard affiliates in the US; McMaster University in Canada; source author affiliations.
English catalogue narrative
Halicin: AI helps find an antibacterial lead
Combined neural-network screening with laboratory experiments to identify antibacterial activity of halicin, including activity in the reported mouse infection models.
Scientific scope
A repurposed molecule with preclinical evidence is not an approved antibiotic or proof of effectiveness in humans. Human teams selected candidates and ran the experiments.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Jonathan M. Stokes; Kevin Yang; Kyle Swanson; Wengong Jin; Andres Cubillos-Ruiz; Nina M. Donghia; Craig R. MacNair; Shawn French; Lindsey A. Carfrae; Zohar Bloom-Ackermann; Victoria M. Tran; Anush Chiappino-Pepe; Ahmed H. Badran; Ian W. Andrews; Emma J. Chory; George M. Church; Eric D. Brown; Tommi S. Jaakkola; Regina Barzilay; James J. Collins
Full byline of the specified publication; original laboratory roles remain in the source.
Halicin discovery pipeline / MIT-led research collaboration / Directed message-passing neural network: Molecular activity prediction; human-led experiments and interpretation (source reported).