How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules

Drug-resistant microbes including bacteria, fungi, parasites, and viruses are a growing global threat. About five million deaths in 2021 were associated⁠(opens in a new window) with bacterial antimicrobial resistance—an annual toll projected to roughly double by 2050. It can take years to find molecules with the potential to become antimicrobials. Researchers are using AI to accelerate this early stage of discovery.

“Antimicrobial resistance is one of the greatest existential threats to humanity in my opinion,” said César de la Fuente⁠(opens in a new window), a bioengineer whose cross-disciplinary lab searches for antimicrobial candidates. “And yet, we haven’t had a new class of antibiotics for 50 years.”

Much of modern antimicrobial development focuses on modifying existing medicines or searching familiar classes of chemicals. But that approach offers diminishing returns.

De la Fuente’s lab starts somewhere far less explored: the code of life. The central idea behind the work is that biology is an information system. “The nucleotides that make up DNA, and the amino acids that make up proteins and peptides are sort of like an alphabet,” said de la Fuente. “Thinking about biology as information enabled us to develop methods that can begin to decipher the organizing principles of life that gave rise to a functional molecule.”

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. The approach can reduce the initial search for candidate molecules from years to hours.

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.

Exploring biology’s unread spaces

But identifying a promising candidate doesn’t necessarily mean that it will become an effective medicine.