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COMPUTATIONAL CHEMISTRY

COVID-19


TWO GROUPS USE ARTIFICIAL INTELLIGENCE TO FIND COMPOUNDS THAT COULD FIGHT THE
NOVEL CORONAVIRUS


ONE GROUP IDENTIFIES AN EXISTING DRUG, THE OTHER FINDS 6 NOVEL MOLECULES, BUT
THE CONSEQUENCES OF REPORTING POSSIBLY HELPFUL MOLECULES ARE UNCLEAR

BY SAM LEMONICK

FEBRUARY 4, 2020 | A VERSION OF THIS STORY APPEARED IN VOLUME 98, ISSUE 6





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Just over a month after the first reports of a novel coronavirus spreading in
China, two independent groups this week reported that they have used artificial
intelligence in different ways to find possible treatments for the virus. On
Tuesday, researchers from the AI drug discovery company BenevolentAI and
Imperial College London reported they have used AI software to find an
already-approved drug that might limit the virus’s ability to infect people
(Lancet 2020, DOI: 10.1016/S0140-6736(20)30304-4). On Thursday, the company
Insilico Medicine announced its AI algorithms had designed six new molecules
that could stop the virus from replicating in people’s bodies.

As news of the virus, called 2019-nCoV, spread since the end of December,
scientists raced to learn more about it and find ways to fight it. Researchers
in China published the first genomic sequence of the virus in mid-January, and
by Jan. 29 scientists suggested that the virus might enter human cells by
binding to a cell-surface molecule called angiotensin-converting enzyme 2, or
ACE2 (bioRxiv 2020, DOI: 10.1101/2020.01.26.919985). The two AI drug discovery
groups used this and other data to teach their software how to target the virus.

BenevolentAI’s algorithms connect molecular structure data to biomedical
information about relevant receptors and diseases to find potential drug
targets. The group adapted its search to the newly available information about
2019-nCoV and ACE2, and the software pointed to the enzyme adaptor-associated
protein kinase 1 (AAK1) as a possible target for the disease. AAK1 regulates
endocytosis, the process that brings material into cells, which also is a common
mode of viral infection.



Next, the software and the researchers winnowed down 378 known AAK1 inhibitors
to one possible drug, baricitinib, based on its affinity for the kinase and its
toxicity. Baricitinib is approved to treat rheumatoid arthritis. The team
rejected two cancer drugs that also might be effective because of their side
effects and high predicted effective dosages. The researchers propose testing
the drug in clinical trials against the virus.

But, in the paper, they also caution that their suggestion should not be taken
as medical advice about 2019-nCoV treatment or prevention. They write that they
published their research “to assist in the global response” to the virus.

Support nonprofit science journalism
C&EN has made this story and all of its coverage of the coronavirus epidemic
freely available during the outbreak to keep the public informed. To support us:
Donate Join Subscribe

AI-based efforts like this could help to conserve drug researchers’ time and
resources, says Mike Tarselli, scientific director of the Society for Laboratory
Automation and Screening. “The use of AI to augment human capacity, to address a
pressing public health concern using existing data without re-deploying a full
team, should be a boon to researchers.” The BenevolentAI report is “more a
testimony to good literature searching and curation” than “a triumph of
artificial intelligence,” according to veteran drug-discovery researcher and
blogger Derek Lowe. He says anyone could search through the extensive kinase
literature to find good drug candidates, but he acknowledges that the
researchers likely sped up their search with a well-organized database and
software good at searching through it.

Related: What we do and don't know about the novel coronavirus

Timothy Cernak, a medicinal chemist at the University of Michigan, agrees with
Lowe that most medicinal chemists could have probably identified this molecule
as a potential drug using traditional database searches. He points out that as a
class, kinase inhibitor drugs are known for targeting many different enzymes,
meaning it wouldn’t be that hard to find one that acted against AAK1. He adds
that the paper does not make clear how the researchers’ software decided to
focus on that kinase, which could be a surprising result revealed by the AI.

Cernak says he’s both excited and scared by how quickly scientists have
identified and published potential treatments for 2019-nCoV. He wonders what
might happen if people worried about the virus take baricitinib without medical
supervision; the US Food and Drug Administration describes the drug as a potent
immunosuppressant with side effects that include serious or fatal infections.
But he acknowledges that the researchers likely had few options except
publishing what they found: “We’re in a global emergency, so we need answers.”

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Insilico scientists took another approach to finding possible 2019-nCoV
treatments. On Thursday, they released a paper describing a weeklong process
using their AI algorithms to design new molecules that could limit 2019-nCoV’s
ability to replicate in cells. Researchers studying the virus have already
proposed doing so with approved antiviral drugs called 3C-like protease
inhibitors. This protease helps the virus to communicate replication
instructions to its host cell. The Insilico group argues that current knowledge
of the virus protease suggests that those existing drugs would require
unreasonably high dosages to be effective.



Instead, they set out to find possible novel drugs. The company’s AI algorithms
evaluated the structure of the protease binding pocket and a representative
inhibitor ligand for a virus that is nearly identical to 2019-nCoV, the
coronavirus that caused severe acute respiratory syndrome (SARS) in 2003. The
group programmed the algorithms to have a preference for novel structures with
elements similar to known drugs. The paper describes six molecules that the
group says are significantly different from known drugs but are predicted to be
effective 3C-like protease inhibitors. The paper has been submitted to the
bioRxiv preprint server but has not yet been accepted.

Five of the six do appear to be unique, according to Todd Wills, Managing
Director at CAS, a division of the American Chemical Society, which publishes
C&EN. The other has a central motif first described in 1910, he says, and which
appears in the drug praziquantel.

The researchers say that they plan to start synthesizing and testing their
molecules but that they are looking for outside partners who might want to help
with those steps. Lowe is skeptical that these compounds will become medicines.
“I would be surprised if anyone is willing to pony up enough money” to put
Insilico’s molecules through a full drug development program, he says,
considering that other, more proven drug design approaches are already underway
for 2019-nCoV.

Insilico’s molecules appear to be made in part from reshuffled known drug
motifs, says Ingo Hartung, director of medicinal chemistry at Merck KGaA, but
that’s neither bad nor unexpected. Medicinal chemists often use such an
approach. The promise of AI, he adds, is it will speed up the process of
designing, testing, and making potential new drugs, which appears to be
happening in this case.

What is needed now, Hartung says, is experimental data to evaluate the
molecules: “Coronavirus patients don’t care how somebody came up with their drug
but that the drug is efficacious and safe.”

UPDATE

On Feb. 11, 2020, the Coronavirus Study Group of the International Committee on
Taxonomy of Viruses officially named the novel coronavirus "severe acute
respiratory syndrome coronavirus 2" (SARS-CoV-2). The temporary name for the
virus was 2019-nCoV.

This story was updated on Feb. 6, 2020, to include information about a second
effort to find possible coronavirus drugs with artificial intelligence and to
clarify a comment by Timothy Cernak.


Chemical & Engineering News
ISSN 0009-2347
Copyright © 2022 American Chemical Society


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