SAN FRANCISCO, June 18 (Reuters) - SandboxAQ, an

artificial intelligence startup spun out of Alphabet's

Google and backed by Nvidia ( NVDA ), on Wednesday released a

trove of data it hopes will speed up the discovery of new

medical treatments by helping scientists understand how drugs

stick to proteins.

The goal is to help scientists predict whether a drug will

bind to its target in the human body.

But while the data is backed up by real-world scientific

experiments, it did not come from a lab. Instead, SandboxAQ,

which has raised nearly $1 billion in venture capital, generated

the data using Nvidia's ( NVDA ) chips and will feed it back into AI

models that it hopes scientists can use to rapidly predict

whether a small-molecule pharmaceutical will bind to the protein

that researchers are targeting, a key question that must be

answered before a drug candidate can move forward.

For example, if a drug is meant to inhibit a biological

process like the progression of a disease, scientists can use

the tool to predict whether the drug molecule is likely to bind

to the proteins involved in that process.

The approach is an emerging field that combines traditional

scientific computing techniques with advancements in AI. In many

fields, scientists have long had equations that can precisely

predict how atoms combine into molecules.

But even for relatively small three-dimensional

pharmaceutical molecules, the potential combinations become far

too vast to calculate manually, even with today's fastest

computers. So SandboxAQ's approach was to use existing

experimental data to calculate about 5.2 million new,

"synthetic" three-dimensional molecules - molecules that haven't

been observed in the real world, but were calculated with

equations based on real-world data.

That synthetic data, which SandboxAQ is releasing publicly,

can be used to train AI models that can predict whether a new

drug molecule is likely to stick to the protein researchers are

targeting in a fraction of the time it would take to calculate

it manually, while retaining accuracy. SandboxAQ will charge

money for its own AI models developed with the data, which it

hopes will get results that rival running lab experiments, but

virtually.

"This is a long-standing problem in biology that we've all,

as an industry, been trying to solve for," Nadia Harhen, general

manager of AI simulation at SandboxAQ, told Reuters on Tuesday.

"All of these computationally generated structures are tagged to

a ground-truth experimental data, and so when you pick this data

set and you train models, you can actually use the synthetic

data in a way that's never been done before."