For decades, discovering novel antibiotics meant digging through the same patch of dirt. Biologists spent countless hours screening soil-dwelling microbes for properties known to kill harmful bacteria. But as superbugs resistant to existing antibiotics have spread widely, breakthroughs were becoming as rare as new places to dig.
Now, artificial intelligence is giving scientists a reason to dramatically expand their search into databases of molecules that look nothing like existing antibiotics.
A study published Thursday in the journal Cell describes how researchers at the Massachusetts Institute of Technology used machine learning to identify a molecule that appears capable of countering some of the world’s most formidable pathogens.
When tested in mice, the molecule, dubbed halicin, effectively treated the gastrointestinal bug Clostridium difficile (C. diff), a common killer of hospitalized patients, and another type of drug-resistant bacteria that often causes infections in the blood, urinary tract, and lungs.
The most surprising feature of the molecule? It is structurally distinct from existing antibiotics, the researchers said. It was found in a drug-repurposing database where it was initially identified as a possible treatment for diabetes, a feat that showcases the power of machine learning to support discovery efforts.
“Now we’re finding leads among chemical structures that in the past we wouldn’t have even hallucinated that those could be an antibiotic,” said Nigam Shah, professor of biomedical informatics at Stanford University. “It greatly expands the search space into dimensions we never knew existed.”
Shah, who was not involved in the research, said that the generation of a promising molecule is just the first step in a long and uncertain process of testing its safety and effectiveness in humans.
But the research demonstrates how machine learning, when paired with expert biologists, can speed up time-consuming preclinical work, and give researchers greater confidence that the molecule they’re examining is worth pursuing through more costly phases of drug discovery.
That is an especially pressing challenge in the development of new antibiotics, because a lack of economic incentives has caused pharmaceutical companies to pull back from the search for badly needed treatments. Each year in the U.S., drug-resistant bacteria and fungi cause more than 2.8 million infections and 35,000 deaths, with more than a third of fatalities attributable to C. diff, according to the the Centers for Disease Control and Prevention.
The damage is far greater in countries with fewer health care resources.
Without the development of novel antibiotics, the World Health Organization estimates that the global death toll from drug resistant infections is expected to rise to 10 million a year by 2050, up from about 700,000 a year currently.
In addition to finding halicin, the researchers at MIT reported that their machine learning model identified eight other antibacterial compounds whose structures differ significantly from known antibiotics.
“I do think this platform will very directly reduce the cost involved in the discovery phase of antibiotic development,” said James Collins, a co-author of the study who is a professor of bioengineering at MIT. “With these models, one can now get after novel chemistries in a shorter period of time involving less investment.”
The machine learning platform was developed by Regina Barzilay, a professor of computer science and artificial intelligence who works with Collins as co-lead of the Jameel Clinic for Machine Learning in Health at MIT. It relies on a deep neural network, a type of AI architecture that uses multiple processing layers to analyze different aspects of data to deliver an output.
Prior types of machine learning systems required close supervision from humans to analyze molecular properties in drug discovery and produced spotty results. But Barzilay’s model is part of a new generation of machine learning systems that can automatically learn chemical properties connected to a specific function, such as an ability to kill bacteria.
Barzilay worked with Collins and other biologists at MIT to train the system on more than 2,500 chemical structures, including those that looked nothing like antibiotics. The effect was to counteract bias that typically trips up most human scientists who are trained to look for molecular structures that look a lot like other antibiotics.
The neural net was able to isolate molecules that were predicted to have antibacterial qualities but didn’t look like existing antibiotics, resulting in the identification of halicin.
“To use a crude analogy, it’s like you show an AI all the different means of transportation, but you’ve not shown it an electric scooter,” said Shah, the bioinformatics professor at Stanford. “And then it independently looks at an electronic scooter and says, ‘Yeah, this could be useful for transportation.’”
In follow-up testing in the lab, Collins said, halicin displayed a remarkable ability to fight a wide range of multidrug-resistant pathogens. Tested against 36 such pathogens, it displayed potency against 35 of them. Collins said testing in mice showed “excellent activity” against C. diff, tuberculosis, and other bacteria.
The ability to identify molecules with specific antibiotic properties could aid in the development of drugs to treat so-called orphan conditions that affect a small percentage of the population but are not targeted by drug companies because of the lack of financial rewards.
Collins noted that commercializing halicin would take many months of study to evaluate its toxicity in humans, followed by multiple phases of clinical trials to establish safety and efficacy.