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.
