A.I. Is Not Going to Magically Deliver a Coronavirus Vaccine
The discovery of a chemical compound with antibiotic properties is a helpful case study in the potential — and limits — of using A.I. to develop new treatments
In late February, a paper appeared in the journal Cell with encouraging news regarding one of the world’s most persistent public health problems. Researchers at Massachusetts Institute of Technology and Harvard University had used artificial intelligence to identify a chemical compound with powerful antibiotic properties against some of the world’s most drug-resistant strains of bacteria — a welcome discovery in a world where 700,000 people die every year from drug-resistant infections. It was the first time an antibacterial compound had been identified this way. The researchers named it halicin, in honor of the computer HAL in the film 2001: Space Odyssey.
While the global need for new antibiotics to treat drug-resistant infections is as pressing as it was at the start of the year, the world’s attention has been diverted by the novel coronavirus pandemic, and the hunt for a vaccine that can halt Covid’s spread. Like new antibiotics, new vaccines typically take up to 10 years to deliver. In the case of the Covid vaccine, scientists are working frantically to shorten that timeline. Machine learning has been a vital tool in the fight against Covid, with algorithms assisting in sorting through massive piles of accumulating data and narrowing down potential candidates for vaccines.
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The halicin paper is a helpful case study in how machine learning can be a powerful asset in finding elusive treatments. It also highlights artificial intelligence’s limits. Identifying a new antibiotic compound like halicin is only a first small step in the years-long process of evaluating whether that compound can be developed into a safe and effective antibiotic drug. A vaccine is an exponentially more complicated drug than an antibiotic, and while algorithms can point…