OpenAI's researchers are using Codex and ChatGPT to search for new antimicrobial molecules, reducing the initial discovery phase from years to hours. The project, led by bioengineer César de la Fuente, aims to address the growing threat of antimicrobial resistance, which caused five million deaths in 2021.
The lab's deep-learning models are trained to recognize patterns in biological sequences, allowing them to search vast genome and protein datasets for potential antimicrobials. This approach can significantly cut down the time required to identify candidate molecules, which previously took years to find.
Alongside its own AI models, the lab uses ChatGPT and Codex to brainstorm hypotheses, write and refine code, process datasets, analyze results, and connect ideas across scientific disciplines. This collaboration helps bridge gaps between different scientific fields, enabling more efficient and interdisciplinary research.
"Antimicrobial resistance is one of the greatest existential threats to humanity in my opinion," said César de la Fuente, a bioengineer whose cross-disciplinary lab searches for antimicrobial candidates. "And yet, we haven’t had a new class of antibiotics for 50 years."
The challenge is to identify patterns that make a molecule functional, or biologically active, then determine which have the potential to combat infectious microbes. Scientists have long searched for antimicrobials in plants, animals, microbes, insects, water, and soil, but the process remains slow and iterative.
Identifying a promising candidate doesn’t necessarily mean that it will become an effective medicine. Scientists must first confirm that a candidate molecule kills the target microbe, determine the amount needed in order to be effective, and test how it affects human cells. Chemists may then optimize it to improve its effectiveness, safety, or stability.
Source: openai
