Microsoft's New AI System To Help Decode Protein Motion, Advance Drug Discovery: Satya Nadella

(MENAFN- IANS) New Delhi, July 11 (IANS) Microsoft's new AI system BioEmu will help decode protein motion and help in faster discovery of drugs, said CEO Satya Nadella on Friday.

Biomolecular Emulator-1 (BioEmu-1) is a deep learning model that can generate thousands of protein structures per hour on a single graphics processing unit (GPU).

“Understanding protein motion is essential to understanding biology and advancing drug discovery,” said Nadella, in a post on social media platform X.

Sharing a research paper on the model, Nadella added that“today we're introducing BioEmu, an AI system that emulates the structural ensembles proteins adopt, delivering insights in hours that would otherwise require years of simulation”.

Proteins play an essential role -- from forming muscle fibers to protecting against diseases -- in almost all biological processes in both humans and other life forms.

While recent years have seen progress in better understanding of the protein structures, predicting a single protein structure from its amino acid sequence was not feasible.

But, with BioEmu-1, scientists can get a glimpse into the rich world of different structures each protein can adopt, or structural ensembles. This enables them to get a deeper understanding of how proteins work -- critical for designing more effective drugs.

“BioEmu integrates over 200 milliseconds of molecular dynamics (MD) simulations, static structures, and experimental protein stabilities using novel training algorithms. It captures diverse functional motions --including cryptic pocket formation, local unfolding, and domain rearrangements -- and predicts relative free energies with 1 kcal/mol accuracy compared to millisecond-scale MD and experimental data,” revealed scientists from AI for Science at Microsoft Research, in the paper published in the journal Science.

The team noted that BioEmu provides "mechanistic insights by jointly modelling structural ensembles and thermodynamic properties".

The approach pays off the cost of MD and experimental data generation, demonstrating a scalable path toward understanding and designing protein function, they added.

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