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.

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.

“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.

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Machine learning algorithms that can predict yeast metabolism from its protein content have been developed by scientists at the Francis Crick Institute. The findings could provide a basis for brewers to have greater control over the flavour of their beer, and scientists to personalise treatments for metabolic disorder patients, in the future.

Metabolism is the process by which organisms convert nutrients into energy and essential molecules, via a series of chemical reactions. When yeast metabolises sugar in the absence of oxygen, it ‘ferments’ to produce alcohol, acids and gases, including flavour compounds, that make bread, wine and beer taste good.

Within a cell, metabolism produces hundreds of small molecules, called metabolites. Although yeast is evolutionarily very distant to humans, many of these metabolites are identical, and are made in a similar way. Until now, however, the mechanisms controlling metabolism have not been fully understood.

The latest study, published in Cell Systems, shows that to a large extent, the metabolism of brewer’s yeast (S. cerevisiae) is predictable by machine learning algorithms, if they are provided with large amounts of protein expression information.

“Thanks to machine learning, we now have a better understanding of what controls metabolism, which is good news for brewers looking to create the perfect pint, or for Biotechnologists that use yeast to produce vaccines and other proteins that are medically important ” says Aleksej Zelezniak, first author of the paper and researcher at the Crick, and has recently moved to Sweden to establish his independent research group at the Chalmers University of Technology.

Linking proteins and metabolites

Until now, scientists have been divided over whether metabolism is self-regulating or controlled by gene expression changes; partly because existing methods have failed to detect any strong correlation between the read-out of genes—proteins—and metabolites.

In this study, scientists quantified enzyme expression in 97 different strains of S. cerevisiae, known to show differences in metabolism, linking it to changes in metabolite concentrations measured.

They developed machine learning algorithms that could pick up complex relationships between changes in gene expression and metabolites produced. They found that metabolism was controlled by lots of enzymes acting in concert—with no single enzyme having a major effect by itself.

“The relationship between enzyme expression and metabolism in yeast is so complex that previous models have failed to detect it,” says Markus Ralser, group leader at the Crick and senior author of the paper. “Changes in cellular metabolism are tightly bound to disorders that increase with age, including diabetes, various types of cancer, and neurodegenerative diseases. The fact that one can start to predict metabolism in simple cells like yeast cells, is a milestone for the effort to soon be able to predict metabolism also in human tissues.

“Similar Computational tools are used by tech giants like Amazon and Facebook all the time. But instead of using them to tailor advertisements or recommend friends, we’ve harnessed their power to predict a yeast cell’s metabolism. These insights not only inform our understanding of the basis of beer flavouring, but also some human disorders of metabolism.”

From beer to personalised medicine

The team is hoping to transfer their findings in yeast cells to the clinic in the next few years to help patients with metabolic diseases.

“For non-biologists it might seem strange that one can transfer our knowledge of yeast to humans, but in reality, many fundamental principles of what we know about human biology came from yeast research,” says Aleksej.

“We currently expand our algorithms, so that they will provide us with information also about a person’s metabolism, based on which proteins are present in their blood. This information could help doctors decide which treatment option is best for an individual patient.”

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