听力课堂TED音频栏目主要包括TED演讲的音频MP3及中英双语文稿，供各位英语爱好者学习使用。本文主要内容为演讲MP3+双语文稿:我们如何利用人工智能来发现新的抗生素，希望你会喜欢！

【演讲者及介绍】Jim Collins

合成生物学的创始人之一，生物工程师Jim Collins热衷于在教室和实验室里教学并激励年轻人。

【演讲主题】我们如何利用人工智能来发现新的抗生素

How we're using AI to discover new antibiotics

【中英文字幕】

翻译者 Jinhao Ma 校对者 Wanting Zhong

So how are we going to beat this novel coronavirus? By using our best tools: our science and our technology. In my lab, we're using the tools of artificial intelligence and synthetic biology to speed up the fight against this pandemic. Our work was originally designed to tackle the antibiotic resistance crisis. Our project seeks to harness the power of machine learning to replenish our antibiotic arsenal and avoid a globally devastating postantibiotic era. Importantly, the same technology can be used to search for antiviral compounds that could help us fight the current pandemic.

我们要如何击败新型冠状病毒？通过使用我们最好的工具：我们的科学和技术。在我的实验室中，我们正在使用人工智能和合成生物学的工具，加快与这场疫情的战斗。我们工作的初衷是想解决抗生素耐药性的危机。我们的项目试图利用机器学习的力量补充我们的抗生素“弹药库”，并避免会造成全球性危害的后抗生素时代。重要的是，同样的技术能用来寻找可以帮助我们应对当前疫情的抗病毒化合物。

Machine learning is turning the traditional model of drug discovery on its head. With this approach, instead of painstakingly testing thousands of existing molecules one by one in a lab for their effectiveness, we can train a computer to explore the exponentially larger space of essentially all possible molecules that could be synthesized, and thus, instead of looking for a needle in a haystack, we can use the giant magnet of computing power to find many needles in multiple haystacks simultaneously.

机器学习正在颠覆传统的药物开发模型。通过这种方法，我们不再需要在实验室里一个接一个费力地测试成千上万现有分子的效力，而是可以训练电脑探索更大的、基本上涵盖了所有可能合成的分子的空间。因此，相比在“海底捞针”，我们可以使用计算能力这块“巨型磁铁”，同时在几个“海”底捞很多很多根“针”。

We've already had some early success. Recently, we used machine learning to discover new antibiotics that can help us fight off the bacterial infections that can occur alongside SARS-CoV-2 infections. Two months ago, TED's Audacious Project approved funding for us to massively scale up our work with the goal of discovering seven new classes of antibiotics against seven of the world's deadly bacterial pathogens over the next seven years. For context: the number of new class of antibiotics that have been discovered over the last three decades is zero.

我们的早期尝试已经取得了一些成功。最近，我们使用机器学习发现了新的抗生素，可以帮助我们抵御可能伴随SARS-CoV-2冠状病毒感染发生的细菌感染。两个月前，TED的“大胆计划”（AudaciousProject）批准了我们的资金申请，这将大规模扩展我们的工作，目标是在未来的七年里，发现七类新型抗生素，以对抗世界上七种致命的病原体细菌。在此说明一下：在过去三十年内，人类发现的新型抗生素的数量为零。

While the quest for new antibiotics is for our medium-term future, the novel coronavirus poses an immediate deadly threat, and I'm excited to share that we think we can use the same technology to search for therapeutics to fight this virus. So how are we going to do it? Well, we're creating a compound training library and with collaborators applying these molecules to SARS-CoV-2-infected cells to see which of them exhibit effective activity. These data will be use to train a machine learning model that will be applied to an in silico library of over a billion molecules to search for potential novel antiviral compounds. We will synthesize and test the top predictions and advance the most promising candidates into the clinic.

虽说寻找新的抗生素是为了我们的中期未来，新型冠状病毒构成了迫在眉睫的致命威胁，我很高兴能跟大家宣布，我们认为可以使用相同的技术寻找对抗这种病毒的治疗手段。那么我们该怎么做呢？我们正在创建一个化合物训练库，并与合作者一起，用这些分子处理被SARS-CoV-2感染的细胞，看看哪个分子表现出了有效的活性。这些数据将用于训练一个机器学习模型，这个模型将被应用于包含超过十亿个分子的计算机模拟数据库，以寻找潜在的新型抗病毒化合物。我们将合成并测试算法预测出的最优分子，并让最有潜力的备选分子进入临床实验。

Sound too good to be true? Well, it shouldn't. The Antibiotics AI Project is founded on our proof of concept research that led to the discovery of a novel broad-spectrum antibiotic called halicin. Halicin has potent antibacterial activity against almost all antibiotic-resistant bacterial pathogens, including untreatable panresistant infections. Importantly, in contrast to current antibiotics, the frequency at which bacteria develop resistance against halicin is remarkably low. We tested the ability of bacteria to evolve resistance against halicin as well as Cipro in the lab. In the case of Cipro, after just one day, we saw resistance. In the case of halicin, after one day, we didn't see any resistance. Amazingly, after even 30 days, we didn't see any resistance against halicin.

听起来是不是过于美好了？并非如此。抗生素人工智能项目的设立是基于我们的概念验证研究，这项研究最终发现了一种新型广谱抗生素，叫做Halocin。Halocin具有强大的抗菌活性，能杀死几乎所有对抗生素耐药的病原体细菌，包括无法治疗的多重耐药感染。重要的是，与目前的抗生素相比，细菌对Halocin产生耐药性的频率非常低。我们在实验室里测试了细菌对Halocin以及环丙沙星（Cipro）产生耐药性的能力。结果发现，仅仅一天后，细菌就对环丙沙星产生了耐药性。而对于Halocin，经过一天后，细菌没有产生任何耐药性。不可思议的是，甚至在30天后，我们也没有发现细菌对Halocin产生任何耐药性。

In this pilot project, we first tested roughly 2,500 compounds against E. coli. This training set included known antibiotics, such as Cipro and penicillin, as well as many drugs that are not antibiotics. These data we used to train a model to learn molecular features associated with antibacterial activity. We then applied this model to a drug-repurposing library consisting of several thousand molecules and asked the model to identify molecules that are predicted to have antibacterial properties but don't look like existing antibiotics. Interestingly, only one molecule in that library fit these criteria, and that molecule turned out to be halicin. Given that halicin does not look like any existing antibiotic, it would have been impossible for a human, including an antibiotic expert, to identify halicin in this manner. Imagine now what we could do with this technology against SARS-CoV-2.

在这个试点项目中，我们首先对大肠杆菌测试了大约2500种化合物。这个训练集包括了已知的抗生素，例如环丙沙星和青霉素，以及许多不是抗生素的药物。我们用这些数据来训练模型，让它学习与抗菌活性有关的分子特征。然后我们把这个模型应用到由数千个分子组成的药物再定位数据库上，并要求模型识别被预测具有抗菌性能但长得不像现有抗生素的分子。有趣的是，数据库里只有一个分子符合这些条件，那个分子就是Halocin。由于Halocin看起来不像任何现有的抗生素，人类，包括抗生素专家，都不可能以这种方式发现Halocin的。想象一下，我们能如何使用这项技术对抗SARS-CoV-2。

And that's not all. We're also using the tools of synthetic biology, tinkering with DNA and other cellular machinery, to serve human purposes like combating COVID-19, and of note, we are working to develop a protective mask that can also serve as a rapid diagnostic test. So how does that work? Well, we recently showed that you can take the cellular machinery out of a living cell and freeze-dry it along with RNA sensors onto paper in order to create low-cost diagnostics for Ebola and Zika. The sensors are activated when they're rehydrated by a patient sample that could consist of blood or saliva, for example. It turns out, this technology is not limited to paper and can be applied to other materials, including cloth. For the COVID-19 pandemic, we're designing RNA sensors to detect the virus and freeze-drying these along with the needed cellular machinery into the fabric of a face mask, where the simple act of breathing, along with the water vapor that comes with it, can activate the test. Thus, if a patient is infected with SARS-CoV-2, the mask will produce a fluorescent signal that could be detected by a simple, inexpensive handheld device. In one or two hours, a patient could thus be diagnosed safely, remotely and accurately.

还不止这些。我们也在使用合成生物学的工具修补DNA和其他细胞成分，为人类服务，比如对抗COVID-19。值得一提的是，我们正在努力开发可作为快速诊断测试的防护口罩。它的原理是什么？我们最近发现你可以从活细胞中提取出细胞成分，然后把它连同RNA检测器在试纸上进行冷冻干燥，从而制作出廉价的埃博拉和寨卡病毒诊断测试工具。在通过添加患者的样本，如血液或唾液进行重新溶解后，RNA检测器就能被激活。事实证明，除了纸制品，这项技术还可以应用于其他材料，包括布料。对于COVID-19疫情，我们正在设计针对病毒的RNA检测器，然后把它们和所需的细胞成分一起在口罩的面料上进行冷冻干燥，简单的呼吸行为连同呼出的水蒸气，就可以激活测试。如果患者感染了SARS-CoV-2，口罩就会产生荧光信号，可以通过简单廉价的手持设备检测出来。一两个小时内，病人就能得到安全、准确、无接触的诊断。

We're also using synthetic biology to design a candidate vaccine for COVID-19. We are repurposing the BCG vaccine, which had been used against TB for almost a century. It's a live attenuated vaccine, and we're engineering it to express SARS-CoV-2 antigens, which should trigger the production of protective antibodies by the immune system. Importantly, BCG is massively scalable and has a safety profile that's among the best of any reported vaccine.

我们也在使用合成生物学设计COVID-19的备选疫苗。我们正在重新利用卡介苗，这种疫苗在近一个世纪前就被用来预防结核病。这是一种减毒活疫苗，我们通过生物工程让它表达SARS-CoV-2抗原，以此来触发免疫系统产生保护性抗体。重要的是，卡介苗可大规模生产，并且它的安全性在所有有记录的疫苗中是最好的。

With the tools of synthetic biology and artificial intelligence, we can win the fight against this novel coronavirus. This work is in its very early stages, but the promise is real. Science and technology can give us an important advantage in the battle of human wits versus the genes of superbugs, a battle we can win.Thank you.

借助合成生物学与人工智能的工具，我们可以打赢和新型冠状病毒的战争。这项工作尚处于初期阶段，但它的前景是真实的。在人类智慧与超级细菌基因的战斗中，科学和技术能给予我们重要的优势，帮助我们取得胜利。谢谢。