The stories are turning into a steady trickle. DeepMind’s neural network again crushes the competition:
DeepMind entered AlphaFold into the Critical Assessment of Structure Prediction (CASP) competition, a biannual protein-folding olympics that attracts research groups from around the world. The aim of the competition is to predict the structures of proteins from lists of their amino acids which are sent to teams every few days over several months. The structures of these proteins have recently been cracked by laborious and costly traditional methods, but not made public. The team that submits the most accurate predictions wins.Google’s DeepMind predicts 3D shapes of proteins
On its first foray into the competition, AlphaFold topped a table of 98 entrants, predicting the most accurate structure for 25 out of 43 proteins, compared with three out of 43 for the second placed team in the same category.
The result was initially depressing to at least one scientist:
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 Dr. AlQuraishi, a researcher at Harvard Medical School. “They were way out in front of everyone else.”
. . . . .
After the conference in Cancun, Dr. 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 criticized big pharmaceutical companies like Merck and Novartis, as well as his academic community, for not keeping pace.Making New Drugs With a Dose of Artificial Intelligence
This is good news! Yes, there will be disruption, but we have discovered new tools to crack the most computationally expensive problems. This is tremendous work and heralds a future of dramatic advances in energy, medicine, and automation.