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  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

So how are we going to beat this novel coronavirus?

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How we're using AI to discover new antibiotics | Jim Collins

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    林宜悉 posted on 2020/07/03
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