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      "statement": "# Halicin: AI helps find an antibacterial lead\n\nCombined neural-network screening with laboratory experiments to identify antibacterial activity of halicin, including activity in the reported mouse infection models.\n\n## Scientific scope\n\nA repurposed molecule with preclinical evidence is not an approved antibiotic or proof of effectiveness in humans. Human teams selected candidates and ran the experiments.\n\n## Reading status\n\nThis 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.\n\n## People and systems\n\nJonathan 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\n\nFull byline of the specified publication; original laboratory roles remain in the source.\n\nHalicin discovery pipeline / MIT-led research collaboration / Directed message-passing neural network: Molecular activity prediction; human-led experiments and interpretation (source reported).\n\n## Original sources\n\n- [A Deep Learning Approach to Antibiotic Discovery](https://pmc.ncbi.nlm.nih.gov/articles/8349178/)\n- [Publisher-deposited byline: A Deep Learning Approach to Antibiotic Discovery](https://api.crossref.org/works/10.1016%2Fj.cell.2020.01.021)\n",
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