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.

Only a fraction of a genome has a clearly understood function, and fewer still encode molecules that can fight infectious microbes. 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. They collect samples, isolate or predict candidate molecules, and test them in an iterative process that can take years.

Digital genome and protein databases now let scientists search across the tree of life for new compounds, dramatically expanding the breadth of databases available for exploration. But that abundance of information comes with its own challenges: finding promising signals among an enormous number of possibilities.

AI is particularly well suited to this needle-in-a-haystack task. It can scan huge datasets, identify patterns that might be difficult for researchers to spot, and prioritize a manageable set of candidates for experimental testing.

But 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.

Further tests assess the dose at which the candidate becomes toxic, how readily microbes develop resistance to the molecule, and how the candidate moves through the body. Teams also determine a reliable way to manufacture the molecule. Candidates that clear these hurdles still face regulatory review and clinical trials before they can reach patients as approved antimicrobial drugs.

For de la Fuente, this is why AI and laboratory biology must advance together. “Ground-truth experiments are essential to validate AI predictions,” he said. “This will be critical in the life sciences in the years to come if we are to continue scratching the surface of our understanding of biology, which is the most complex thing out there.”

His lab explores the genomes of living and extinct organisms for candidate molecules. Searching those genomes, understanding how their encoded proteins form and function, and determining what those molecules do requires expertise spanning several fields.

“We’re a highly transdisciplinary group, so we have people from biology, from chemistry, from computer science, from engineering,” said de la Fuente. Some lab members are strong programmers but know less about biology or chemistry and vice versa. Codex and ChatGPT help bridge those gaps, allowing biologists to build programs and programmers to tackle biological problems. In that sense, AI lowers barriers between scientific disciplines.

It helps lab members review unfamiliar topics that they need for their cross-disciplinary work, clarify terminology, compare methods across fields, and organize ideas for drug discovery. Others use AI to download, organize, and pre-process large genome datasets. ChatGPT also lets lab members work in their native languages, lowering barriers to accelerate their workflows.

De la Fuente uses ChatGPT as a brainstorming partner. He continues to develop ideas with colleagues, but he values AI’s ability to help him shape a hypothesis. He also likes that he can access the world’s information with a click of a button.

“Our ChatGPT workspace is receiving input from all these different people that think differently about the problems that we’re trying to tackle,” said de la Fuente. Lab members feed it their good and bad ideas, making it a kind of collaborative sounding board.

But he cautions against relying on AI alone. “Obviously you have to always double-check for accuracy,” de la Fuente said.

Still, he appreciates AI’s ability to help researchers explore the boundaries between scientific disciplines. “That’s where the breakthroughs are waiting to be discovered. They’re essentially at the edges between fields where very few people go.”

He sees this work as part of a much longer scientific tradition. “We have always relied on tools and machines to understand the world around us,” said de la Fuente. “The telescope illuminated the cosmos, and the microscope revealed the world of the invisible.” Machines now help researchers understand, predict, and engineer biology. “That is what our work is all about,” he said.