Using AI to create better, more potent medicines
Novel framework could offer chemists greater drug options

Date:
May 30, 2023
Source:
Ohio State University
Summary:
While it can take years for the pharmaceutical industry to create
medicines capable of treating or curing human disease, a new study
suggests that using generative artificial intelligence could vastly
accelerate the drug-development process.


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FULL STORY ========================================================================== While it can take years for the pharmaceutical industry to create
medicines capable of treating or curing human disease, a new study
suggests that using generative artificial intelligence could vastly
accelerate the drug-development process.

Today, most drug discovery is carried out by human chemists who rely
on their knowledge and experience to select and synthesize the right
molecules needed to become the safe and efficient medicines we depend
on. To identify the synthesis paths, scientists often employ a technique
called retrosynthesis -- a method for creating potential drugs by working backward from the wanted molecules and searching for chemical reactions
to make them.

Yet because sifting through millions of potential chemical reactions can
be an extremely challenging and time-consuming endeavor, researchers at
The Ohio State University have created an AI framework called G2Retro to automatically generate reactions for any given molecule. The new study
showed that compared to current manual-planning methods, the framework
was able to cover an enormous range of possible chemical reactions as
well as accurately and quickly discern which reactions might work best
to create a given drug molecule.

"Using AI for things critical to saving human lives, such as medicine, is
what we really want to focus on," said Xia Ning, lead author of the study
and an associate professor of computer science and engineering at Ohio
State. "Our aim was to use AI to accelerate the drug design process, and
we found that it not only saves researchers time and money but provides
drug candidates that may have much better properties than any molecules
that exist in nature." This study builds on previous research of Ning's
where her team developed a method named Modof that was able to generate molecule structures that exhibited desired properties better than any
existing molecules. "Now the question becomes how to make such generated molecules, and that is where this new study shines," said Ning, also an associate professor of biomedical informatics in the College of Medicine.

The study was published today in the journal Communications Chemistry.

Ning's team trained G2Retro on a dataset that contains 40,000 chemical reactions collected between 1976 and 2016. The framework "learns" from
graph- based representations of given molecules, and uses deep neural
networks to generate possible reactant structures that could be used to synthesize them.

Its generative power is so impressive that, according to Ning, once
given a molecule, G2Retro could come up with hundreds of new reaction predictions in only a few minutes.

"Our generative AI method G2Retro is able to supply multiple different synthesis routes and options, as well as a way to rank different options
for each molecule," said Ning. "This is not going to replace current
lab-based experiments, but it will offer more and better drug options
so experiments can be prioritized and focused much faster." To further
test the AI's effectiveness, Ning's team conducted a case study to see
if G2Retro could accurately predict four newly released drugs already
in circulation: Mitapivat, a medication used to treat hemolytic anemia; Tapinarof, which is used to treat various skin diseases; Mavacamten,
a drug to treat systemic heart failure; and Oteseconazole, used to
treat fungal infections in females. G2Retro was able to correctly
generate exactly the same patented synthesis routes for these medicines,
and provided alternative synthesis routes that are also feasible and synthetically useful, Ning said.

Having such a dynamic and effective device at scientists' disposal
could enable the industry to manufacture stronger drugs at a quicker
pace -- but despite the edge AI might give scientists inside the lab,
Ning emphasizes the medicines G2Retro or any generative AI creates still
need to be validated -- a process that involves the created molecules
being tested in animal models and later in human trials.

"We are very excited about generative AI for medicine, and we are
dedicated to using AI responsibly to improve human health," said Ning.

This research was supported by Ohio State's President's Research
Excellence Program and the National Science Foundation. Other Ohio State co-authors were Ziqi Chen, Oluwatosin Ayinde, James Fuchs and Huan Sun.

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========================================================================== Story Source: Materials provided by Ohio_State_University. Original
written by Tatyana Woodall. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. Ziqi Chen, Oluwatosin R. Ayinde, James R. Fuchs, Huan Sun, Xia Ning.

G2Retro as a two-step graph generative models for retrosynthesis
prediction. Communications Chemistry, 2023; 6 (1) DOI:
10.1038/s42004- 023-00897-3 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/05/230530174302.htm

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