How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules
How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules
研究人员如何利用 Codex 和 ChatGPT 寻找新型抗菌分子
César de la Fuente and his lab probe the genomes of living and extinct organisms for molecules that could help fight drug-resistant infections. César de la Fuente 和他的实验室正在探索现存及已灭绝生物的基因组，以寻找有助于对抗耐药性感染的分子。
Drug-resistant microbes including bacteria, fungi, parasites, and viruses are a growing global threat. About five million deaths in 2021 were associated 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. 包括细菌、真菌、寄生虫和病毒在内的耐药微生物正成为日益严重的全球威胁。2021 年约有 500 万人的死亡与细菌抗菌素耐药性有关，预计到 2050 年，这一年度死亡人数将翻一番。寻找具有抗菌潜力的分子可能需要数年时间，而研究人员正利用人工智能来加速这一早期发现阶段。
“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.” “在我看来，抗菌素耐药性是人类面临的最大生存威胁之一，”生物工程师 César de la Fuente 说道，他的跨学科实验室致力于寻找抗菌候选药物，“然而，我们已经 50 年没有出现过一类新的抗生素了。”
Much of modern antimicrobial development focuses on modifying existing medicines or searching familiar classes of chemicals. But that approach offers diminishing returns. 现代抗菌药物的开发大多集中在改良现有药物或寻找熟悉的化学类别上。但这种方法的效果正日益减弱。
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.” De la Fuente 的实验室从一个鲜有人探索的领域入手：生命密码。这项工作的核心理念是生物学本质上是一个信息系统。“构成 DNA 的核苷酸，以及构成蛋白质和肽的氨基酸，就像是一种字母表，”de la Fuente 说，“将生物学视为信息，使我们能够开发出各种方法，开始破译产生功能性分子的生命组织原则。”
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. 该实验室的深度学习模型经过训练，能够识别生物序列中的模式，从而在庞大的基因组和蛋白质数据集中搜索潜在的抗菌药物。这种方法可以将候选分子的初步搜索时间从几年缩短到几小时。
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. 除了使用自有的 AI 模型外，实验室还利用 ChatGPT 和 Codex 来进行假设头脑风暴、编写和优化代码、处理数据集、分析结果，并跨科学学科整合思路。
Exploring biology’s unread spaces
探索生物学中未被解读的空间
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. 基因组中只有一小部分具有明确的功能，而能够编码对抗感染性微生物分子的部分则更少。挑战在于识别使分子具有功能性或生物活性的模式，然后确定哪些分子具有对抗感染性微生物的潜力。
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. 长期以来，科学家们一直在植物、动物、微生物、昆虫、水和土壤中寻找抗菌药物。他们采集样本、分离或预测候选分子，并通过一个可能耗时数年的迭代过程进行测试。
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. 数字基因组和蛋白质数据库现在让科学家能够跨越生命之树搜索新化合物，极大地扩展了可供探索的数据库范围。但这种海量信息也带来了挑战：如何在无数种可能性中找到有希望的信号。
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. 人工智能非常适合这种“大海捞针”的任务。它可以扫描庞大的数据集，识别研究人员难以发现的模式，并筛选出一组可控的候选对象进行实验测试。
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. 但确定一个有希望的候选分子并不一定意味着它能成为有效的药物。科学家必须首先确认候选分子能杀死目标微生物，确定其有效剂量，并测试其对人体细胞的影响。随后，化学家可能会对其进行优化，以提高其有效性、安全性和稳定性。
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. 进一步的测试将评估候选分子的毒性剂量、微生物对该分子产生耐药性的难易程度，以及该分子在体内的代谢过程。团队还需要确定一种可靠的生产方法。通过这些障碍的候选药物在成为获批的抗菌药物并提供给患者之前，仍需面对监管审查和临床试验。
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.” 对于 de la Fuente 来说，这就是为什么人工智能和实验室生物学必须共同进步的原因。“实地实验对于验证 AI 的预测至关重要，”他说，“如果我们想要继续深入了解生物学——这一最复杂的领域，这在未来几年对生命科学至关重要。”
A continually evolving collaborator
一个不断进化的合作者
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. 他的实验室探索现存和已灭绝生物的基因组以寻找候选分子。搜索这些基因组、理解其编码蛋白质的形成与功能，并确定这些分子的作用，需要跨越多个领域的专业知识。
“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. “我们是一个高度跨学科的团队，成员来自生物学、化学、计算机科学和工程学等领域，”de la Fuente 说。一些实验室成员擅长编程但对生物或化学了解较少，反之亦然。Codex 和 ChatGPT 帮助弥合了这些差距，使生物学家能够编写程序，程序员能够解决生物学问题。从这个意义上说，人工智能降低了科学学科之间的壁垒。
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. 它帮助实验室成员复习跨学科工作所需的陌生课题、澄清术语、比较不同领域的方法，并整理药物发现的思路。其他人则利用 AI 下载、整理和预处理大型基因组数据集。ChatGPT 还允许实验室成员使用母语工作，降低了障碍，从而加速了工作流程。
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. De la Fuente 将 ChatGPT 用作头脑风暴的伙伴。他继续与同事共同构思，但他非常看重 AI 帮助他塑造假设的能力。他还喜欢只需点击一下按钮就能获取全球信息的功能。
“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. “我们的 ChatGPT 工作区接收来自不同背景人员的输入，他们对我们试图解决的问题有不同的思考方式，”de la Fuente 说。实验室成员将好的和坏的想法都输入其中，使其成为一种协作式的“试金石”。
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… 但他提醒不要仅仅依赖人工智能。“显然，你必须始终核实其准确性，”de la Fuente 说。尽管如此，他依然赞赏人工智能的……