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DeepMind approach before the results were published. “Reading the

abstract, I didn’t think ‘Oh, this is completely new’,” he says. “I accepted

they would do well, but I wasn’t expecting them to do as well as they did.”

According to AlQuraishi, the approach was similar to that under-

taken by other labs, but what distinguished the DeepMind process

was that they were able to “execute better”. He points to the strength

of the DeepMind team on the engineering side.

“I think they can work better than academic groups, because

academic groups tend to be very secretive in this field,” AlQuraishi

says. “And so, even though the ideas DeepMind

had in their algorithm were already out there,

and people were trying them independently, no

one had brought it all together.”

AlQuraishi draws a parallel with the academic

community in machine learning, which has

undergone something of an exodus in recent years

to companies like Google Brain, DeepMind and

Facebook, where organisational structures are more

efficient, compensation packages are generous,

and there are computational resources that don’t

necessarily exist at universities.

“Machine learning computer science commu-

nities have experienced that over the last four or

five years,” he says. “Computational biology is just

now waking up to this new reality.”

This echoes the explanation given by the

founders of DeepMind when they sold to Google

in January 2014. The sheer scale of Google’s compu-

tational network would enable the company to

move research forward much more quickly than

if it had to scale organically, and the $500 million

cheque enabled the startup to hire world-class

talent. Hassabis describes a strategy of targeting

individuals who have been identified as a good fit

for specific research areas. “We’ve our roadmap

that informs what subject areas, sub-fields of

AI or neuroscience will be important,” he says.

“And then we go and find the world’s best person

who fits in culturally as well.”

“So far as a company like DeepMind can make a

dent, I think protein folding is a very good place to

start, because it’s a problem that’s very well defined,

there’s useable data, you can almost treat it like a

pure computer science problem,” AlQuraishi says.

“That’s probably not true in other areas of biology.

It’s a lot messier. So, I don’t necessarily think that

the success that DeepMind has had with protein

folding will translate automatically to other areas.”

“It’s a nice problem from a deep learning

perspective, because at enormous expense, enormous

complexity and enormous time [commitment],

people have generated this amazing resource of

proteins that we already understand,” Jumper says.

While progress is being made, scientists warn

against misplaced exuberance. The American

molecular biologist Cyrus Levinthal expressed the

complexity of the challenge in a bracing manner,

noting that it would take longer than the age of the

universe to enumerate all the possible configura-

tions of a typical protein before reaching the right 3D

structure. “The search space is huge,” says Rich Evans,

a research scientist at DeepMind. “It’s bigger than Go.”

Nevertheless, a milestone in the protein-folding

journey was reached in December 2018 at the CASP

(Critical Assessment of Techniques for Protein

Structure Prediction) competition in Cancun,

Mexico – a biennial challenge that provides an

independent way of plotting researchers’ progress.

The aim for competing teams of scientists is to

predict the structure of proteins from sequences

of their amino acids for which the 3D shape is

known but not yet made public. Independent

assessors verify the predictions.

The protein-folding team at DeepMind entered

as a way of benchmarking AlphaFold, the algorithm

it had developed over the previous two years.

In the months leading up to the conference, the

organisers sent data sets to the team members

in King’s Cross, who sent back their predictions

with no sense of how they would fare. In total,

there were 90 protein structures to predict – some

were template-based targets, which use previously

solved proteins as guidance, others were modelled

from scratch. Shortly before the conference, they

received the results: AlphaFold was, on average,

more accurate than the other teams. And some

metrics put DeepMind significantly ahead of the

other teams: for protein sequences modelled from

scratch – 43 of the 90 – AlphaFold made 25 accurate

predictions. The winning margin was striking: its

nearest rival managed three.

Mohammed AlQuraishi, a fellow at the

Laboratory of Systems Pharmacology and the

Department of Systems Biology at Harvard Medical

School, attended the event, and learned about the

‘ Some of

the most

interesting

areas of

science

are in

the gaps

between

• the

confluences

between

subjects’

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