OCT 09, 2020 3:43 PM PDT

Deep Learning Advances Drug Design

WRITTEN BY: Nouran Amin

Computational data is advancing all areas of medicine and pharmaceutical drug development is certainly no exception.  Recent research at Rice University has utilized deep learning methods to understand how drugs in the developmental phase can react in the human body.

"When you're trying to determine if a compound is a potential drug, you have to check for toxicity," says computer scientist, Lydia Kavraki. "You want to confirm that it does what it should, but you also want to know what else might happen."

The new deep learning-based technique, referred to as the ‘Metabolite Translator’, determines unbounded metabolic reactions and can open the doors to future novel therapeutics.  Metabolites are the products of interactions between molecules such as those found in our drugs.

Learn more about the connection between artificial intelligence and metabolites:

"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.

Source: Science Daily

About the Author
  • Nouran earned her BS and MS in Biology at IUPUI and currently shares her love of science by teaching. She enjoys writing on various topics as well including science & medicine, global health, and conservation biology. She hopes through her writing she can make science more engaging and communicable to the general public.
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