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AI designs new drugs based on protein structures

A new AI model developed by chemists at ETH Zurich makes it possible to generate active pharmaceutical ingredients quickly and easily based on a protein’s three-​dimensional surface. The new process could revolutionize drug research.

AI designs new drugs based on protein structures

Researchers have developed a new method for designing drugs from scratch. This method, called deep interactome learning, combines the strengths of graph neural networks and chemical language models. It does not require transfer or reinforcement learning, which are commonly used techniques in artificial intelligence.

The system, named DRAGONFLY, uses deep learning based on interactomes. Interactomes are maps of interactions between molecules in a cell. DRAGONFLY can generate molecules with specific properties, such as desired bioactivity, synthesizability, and novelty.

In a study, the researchers used DRAGONFLY to design ligands for the human peroxisome proliferator-activated receptor gamma (PPARγ) subtype. PPARγ is a protein involved in regulating metabolism. The researchers successfully designed several ligands that bound to PPARγ with high affinity.

Our work has made the world of proteins accessible for generative AI in drug research

said Gisbert Schneider.

This new method has the potential to revolutionize drug discovery. It could allow researchers to design drugs more quickly and efficiently, and to target diseases that are currently untreatable.

Image is a courtesy of ETH Zurich / Gisbert Schneider.

The original story can be accessed here.

The original research article can be accessed here.

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Dr. Ravindra Shinde is the editor-in-chief and the founder of The Science Dev. He is also a research scientist at the University of Twente, the Netherlands. His research interests include computational physics, computational materials, quantum chemistry, and exascale computing. His mission is to disseminate cutting-edge research to the world through succinct and engaging cover stories.

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