A new study used machine learning to predict potential new antibiotics in the global microbiome, which study authors say represents a significant advance in the use of artificial intelligence in antibiotic resistance research.
The report, published Wednesday in the journal Cell, describes the findings of scientists who used an algorithm to mine the “entire microbial diversity we have on Earth — or a large representation of it — and identify nearly 1 million new molecules to find what is encoded or hidden within all that microbial dark matter”, said César de la Fuente, an author of the study and professor at the University of Pennsylvania. De la Fuente directs the Machine Biology Groupwhich aims to use computers to accelerate discoveries in biology and medicine.
Without such an algorithm, De la Fuente said, scientists would have to use traditional methods such as collecting water and soil to find molecules within those samples. This can be challenging because microbes are everywhere – from the ocean to the human gut.
“It would have taken many, many, many, many years to do this, but with an algorithm we can sort through huge amounts of information, and it just speeds up the process,” De la Fuente said.
The research is urgent for public health, the author said, because antimicrobial resistance causes more than 1.2 million deaths in 2019. That number could increase to 10 million deaths annually by 2050, according to the World Health Organization (WHO).
While De la Fuente said he viewed the study, which yielded the “largest antibiotic discovery effort ever,” as a watershed moment in the potential benefits of artificial intelligence for research, he acknowledged that bad actors could potentially ” evolved to design toxins”“.
He said his lab had implemented safeguards to store them and ensure that molecules could not replicate themselves. In particular, biosecurity precautions were not necessary for this study because these were “inert molecules”.
While artificial intelligence has become a hot-button issue in recent years, De la Fuente said he began using AI in antibiotic research about a decade ago.
“We were only able to accelerate the discovery of antibiotics,” said De la Fuente. “So instead of waiting five, six years to come up with one candidate, we can now, on the computer, come up with hundreds of thousands of candidates in just a few hours.”
Before the US Food and Drug Administration approves an antibiotic, it usually undergoes years of study through laboratory research and clinical trials. These different stages can take 10 to 20 years.
For this study, the researchers collected genomes and metagenomes stored in publicly available databases and searched for DNA fragments that might have antimicrobial activity. To validate those predictions, they used chemistry to synthesize 100 of those molecules in the lab and then tested them to see if they could actually kill bacteria, including “some of the most dangerous pathogens in our society,” De la Fuente said.
79% of the molecules, which were representative of the 1m molecules discovered, could kill at least one microbe – meaning they could serve as a potential antibiotic.
Antibiotic resistance is a growing concern due to the misuse and overuse of antimicrobials in humans, animals and plants, according to the WHO.
The study authors made this data and code freely available for anyone to access with the goal of “advancing science and benefiting humanity,” De La Fuente said.
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He hopes that his team and other researchers will do additional research on the top candidates for potential antibiotic drugs. “Then if it goes well, it will go to phase one clinical trials, but we’re still a long way from that,” he said.
This is not the first study in biology to make significant use of AI. Google DeepMind recently released the latest version of AlphaFolda program that predicts how proteins will interact with other molecules and ions, which could lead to breakthroughs in fields as diverse as cancer therapy and tumor resilience.
Lisa Messeri, an anthropologist of technology at Yale University, said that machine learning and AI are “certainly great for some projects in science,” but not for all.
“We simply ask that researchers and research programs continue to be thoughtful about when they choose to apply these methods and not limit projects that do not necessarily require the use of these well-discussed and focused tools,” she said.
Some have raised concerns about AI, including that it could replace humans in certain jobs — specifically in doing scientific research.
De la Fuente argues AI will involve a collaboration between humans and machines.
Anthony Gitter, a University of Wisconsin-Madison associate professor of biostatistics and medical informatics who uses machine learning in biological experiments, says the “significance of the advance” in the Cell paper “was due to bioinformatics research of the best level as opposed to with automated science enabled by AI”.
“The importance of this research is that it successfully leverages widespread microbial genomic data, uses machine learning to identify candidate antimicrobial peptides, and comprehensively studies the predicted peptides computationally and experimentally to show why they are valuable,” Gitter said.
