Making new drugs with a dose of Artificial Intelligence
These Artificial Intelligence techniques can speed up many aspects of drug discovery and in some cases, perform tasks typically handled by scientists.
You can think of it as a World Cup of biochemical research. Every two years, hundreds of scientists enter a global competition. Tackling a biological puzzle they call “the protein folding problem”, they try to predict the three-dimensional shape of proteins in the human body. No one knows how to solve the problem. Even the winners only chip away at it. But a solution could streamline the way scientists create new medicines and fight disease.
Mohammed AlQuraishi, a biologist who has dedicated his career to this kind of research, flew in early December to Cancun, Mexico, where academics were gathering to discuss the results of the latest contest. As he checked into his hotel, a five-star resort on the Caribbean, he was consumed by melancholy. The contest, the Critical Assessment of Structure Prediction, was not won by academics. It was won by DeepMind, the artificial intelligence lab owned by Google’s parent company. “I was surprised and deflated,” said AlQuraishi, a researcher at Harvard Medical School. “They were way out in front of everyone else.”
DeepMind specialises in “deep learning”, a type of artificial intelligence that is rapidly changing drug discovery science. A growing number of companies are applying similar methods to other parts of the long, enormously complex process that produces new medicines. These AI techniques can speed up many aspects of drug discovery and in some cases, perform tasks typically handled by scientists. “It is not that machines are going to replace chemists,” said Derek Lowe, a long-time drug discovery researcher and the author of ‘In the Pipeline’ — a widely-read blog dedicated to drug discovery. “It’s that the chemists who use machines will replace those that don’t.” After the conference in Cancun, AlQuraishi described his experience in a blog post. The melancholy he felt after losing to DeepMind gave way to what he called “a more rational assessment of the value of scientific progress.”
But he strongly criticised big pharmaceutical companies like Merck and Novartis, as well as his academic community, for not keeping pace.
This article originally appeared on economictimes.com. To read the full article, click here.
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