Imagine you’re a fossil hunter. You spend months in the heat of Arizona digging up bones only to find that what you’ve uncovered is from a previously discovered dinosaur.
That’s how the search for antibiotics has panned out recently. The relatively few antibiotic hunters out there keep finding the same types of antibiotics.
With the rapid rise in drug resistance in many pathogens, new antibiotics are desperately needed. It may be only a matter of time before a wound or scratch becomes life-threatening.
Yet few new antibiotics have entered the market of late, and even these are just minor variants of old antibiotics.
While the prospects look bleak, the recent revolution in artificial intelligence (AI) offers new hope. In a study published on Feb. 20 in the journal Cell, scientists from MIT and Harvard used a type of AI called deep learning to discover new antibiotics.
The traditional way of discovering antibiotics – from soil or plant extracts – has not revealed new candidates, and there are many social and economic hurdles to solving this problem, as well.
Some scientists have recently tried to tackle it by searching the DNA of bacteria for new antibiotic-producing genes. Others are looking for antibiotics in exotic locations such as in our noses.
Drugs found through such unconventional methods face a rocky road to reach the market. The drugs that are effective in a petri dish may not work well inside the body.
They may not be absorbed well or may have side effects. Manufacturing these drugs in large quantities is also a significant challenge.
Enter deep learning. These algorithms power many of today’s facial recognition systems and self-driving cars. They mimic how neurons in our brains operate by learning patterns in data.
An individual artificial neuron – like a mini sensor – might detect simple patterns like lines or circles. By using thousands of these artificial neurons, deep learning AI can perform extremely complex tasks like recognizing cats in videos or detecting tumors in biopsy images.
Given its power and success, it might not be surprising to learn that researchers hunting for new drugs are embracing deep learning AI. Yet building an AI method for discovering new drugs is no trivial task. In large part, this is because in the field of AI there’s no free lunch.
The No Free Lunch theorem states that there is no universally superior algorithm. This means that if an algorithm performs spectacularly in one task, say facial recognition, then it will fail spectacularly in a different task, like drug discovery. Hence researchers can’t simply use off-the-shelf deep learning AI.
The Harvard-MIT team used a new type of deep learning AI called graph neural networks for drug discovery. Back in the AI stone age of 2010, AI models for drug discovery were built using text descriptions of chemicals. This is like describing a person’s face through words such as “dark eyes” and “long nose.”
These text descriptors are useful but obviously don’t paint the entire picture. The AI method used by the Harvard-MIT team describes chemicals as a network of atoms, which gives the algorithm a more complete picture of the chemical than text descriptions can provide.
See more details: sciencealart.com