How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules
OpenAI profiles César de la Fuente's lab using ChatGPT and Codex alongside deep-learning models to accelerate antimicrobial molecule discovery.
OpenAI published a case study on bioengineer César de la Fuente's lab, which uses ChatGPT and Codex for hypothesis brainstorming, code writing, dataset processing, and bridging knowledge gaps across biology, chemistry, and computer science. The lab's deep-learning models scan genome and protein databases for antimicrobial peptide candidates, potentially cutting initial searches from years to hours. Bacterial antimicrobial resistance was associated with about five million deaths in 2021, a toll projected to roughly double by 2050.
- Lab treats biology as an information system to mine genomes for antimicrobial candidates
- Deep-learning models can cut initial molecule searches from years to hours
- ChatGPT and Codex support brainstorming, coding, and cross-disciplinary collaboration
- Ground-truth lab experiments remain essential to validate AI-predicted candidates
- Vendors
- OpenAI
- Organizations
- de la Fuente Lab
From the original article
4 min read at openai.com
César de la Fuente’s lab uses Codex and ChatGPT to search living and extinct genomes for antimicrobial candidates to fight drug-resistant infections.
openai.com
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