“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,” says Eleni Litsa
A new deep learning-based tool called Metabolic Translator may soon give researchers a better handle on how drugs in development will perform in the human body.
When you take a medication, you want to know precisely what it does. Pharmaceutical companies go through extensive testing to ensure that you do.
Metabolic Translator, a computational tool that predicts metabolites, the products of interactions between small molecules like drugs and enzymes could help improve the process.
The new tool takes 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 new discoveries.
“When you’re trying to determine if a compound is a potential drug, you have to check for toxicity,” says Lydia Kavraki, a professor of computer science, a professor of bioengineering, mechanical engineering, and electrical and computer engineering, and director of Rice’s Ken Kennedy Institute, as well as coauthor of the new paper in Chemical Science.
“You want to confirm that it does what it should, but you also want to know what else might happen,” she says.
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 form through other enzymes as well.
“Our bodies are networks of chemical reactions,” says graduate student and lead author Eleni Litsa. “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 says.
The rise of machine learning architectures that operate on structured data, such as chemical molecules, make the work possible, she says.
Transformer was introduced in 2017 as a sequence translation method that has found wide use in language translation and 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 says.
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 they trained Metabolite Translator 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 not commonly involved in drug metabolism and 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 says. “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.”
Rice University and the Cancer Prevention and Research Institute of Texas supported the research.