By Mike Williams 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. Lydia Kavraki, Professor of Computer Science, 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.

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.

You are free to share this article under the Attribution 4.0 International license. 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.

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.