Deep learning gives drug design a boost
Ttranslator expands metabolite prediction of chemical reactions in the
human body

Date:
October 5, 2020
Source:
Rice University
Summary:
A computational tool may help pharmaceutical companies expand
their ability to investigate the safety of drugs.



FULL STORY ==========================================================================
When you take a medication, you want to know precisely what it does.

Pharmaceutical companies go through extensive testing to ensure that
you do.


==========================================================================
With a new deep learning-based technique created at Rice University's
Brown School of Engineering, they may soon get a better handle on how
drugs in development will perform in the human body.

The Rice lab of computer scientist Lydia Kavraki has introduced Metabolite Translator, a computational tool that predicts metabolites, the products
of interactions between small molecules like drugs and enzymes.

The Rice researchers take advantage of deep-learning methods and the availability of massive reaction datasets to give developers a broad
picture of what a drug will do. The method is unconstrained by rules
that companies use to determine metabolic reactions, opening a path to
novel discoveries.

"When you're trying to determine if a compound is a potential drug, you
have to check for toxicity," Kavraki said. "You want to confirm that it
does what it should, but you also want to know what else might happen."
The research by Kavraki, lead author and graduate student Eleni Litsa
and Rice alumna Payel Das of IBM's Thomas J. Watson Research Center,
is detailed in the Royal Society of Chemistry journal Chemical Science.

The researchers trained Metabolite Translator to predict metabolites
through any enzyme, but measured its success against the existing
rules-based methods that are focused on the enzymes in the liver. These
enzymes are responsible for detoxifying and eliminating xenobiotics,
like drugs, pesticides and pollutants.

However, metabolites can be formed through other enzymes as well.



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"Our bodies are networks of chemical reactions," Litsa said. "They
have enzymes that act upon chemicals and may break or form bonds that
change their structures into something that could be toxic, or cause
other complications.

Existing methodologies focus on the liver because most xenobiotic
compounds are metabolized there. With our work, we're trying to capture
human metabolism in general.

"The safety of a drug does not depend only on the drug itself but also
on the metabolites that can be formed when the drug is processed in the
body," Litsa said.

The rise of machine learning architectures that operate on
structured data, such as chemical molecules, make the work possible,
she said. Transformer was introduced in 2017 as a sequence translation
method that has found wide use in language translation.

Metabolite Translator is based on SMILES (for "simplified molecular-input
line- entry system"), a notation method that uses plain text rather than diagrams to represent chemical molecules.

"What we're doing is exactly the same as translating a language, like
English to German," Litsa said.



==========================================================================
Due to the lack of experimental data, the lab used transfer learning
to develop Metabolite Translator. They first pre-trained a Transformer
model on 900,000 known chemical reactions and then fine-tuned it with
data on human metabolic transformations.

The researchers compared Metabolite Translator results with those from
several other predictive techniques by analyzing known SMILES sequences
of 65 drugs and 179 metabolizing enzymes. Though Metabolite Translator
was trained on a general dataset not specific to drugs, it performed
as well as commonly used rule-based methods that have been specifically developed for drugs. But it also identified enzymes that are not commonly involved in drug metabolism and were not found by existing methods.

"We have a system that can predict equally well with rule-based systems,
and we didn't put any rules in our system that require manual work and
expert knowledge," Kavraki said. "Using a machine learning-based method,
we are training a system to understand human metabolism without the
need for explicitly encoding this knowledge in the form of rules. This
work would not have been possible two years ago." Kavraki is the Noah
Harding Professor of Computer Science, a professor of bioengineering, mechanical engineering and electrical and computer engineering and
director of Rice's Ken Kennedy Institute. Rice University and the Cancer Prevention and Research Institute of Texas supported the research.


========================================================================== Story Source: Materials provided by Rice_University. Note: Content may
be edited for style and length.


========================================================================== Journal Reference:
1. Eleni E. Litsa, Payel Das, Lydia E. Kavraki. Prediction of drug
metabolites using neural machine translation. Chemical Science,
2020; DOI: 10.1039/D0SC02639E ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201005112122.htm

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