ChatGPT-vel és Codexszel fedeznek fel új antibiotikumokat

César de la Fuente and his lab probe the genomes of living and extinct organisms for molecules that could help fight drug-resistant infections. 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