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La Universidad de Kyoto elige la tecnología Intel Machine y Deep Learning para abordar el descubrimiento de fármacos, la medicina y los desafíos de la asistencia sanitaria (English Only)

Kyoto University Graduate School of Medicine*, one of Asia’s leading research-oriented institutions, has recently chosen Intel® Xeon® Scalable processors to power its clinical genome analysis cluster and its molecular simulation cluster. These clusters will aid in Kyoto’s search for new drug discoveries and help reduce research and development costs.

The Intel Xeon Scalable platform offers potent performance for all types of artificial intelligence (AI). Intel’s optimizations for popular deep learning frameworks have produced up to 127 times1 performance gains for deep learning training and 198 times2 performance gains for deep learning inference for AI workloads running on Intel Xeon Scalable processors. Kyoto is one of many leading healthcare providers and research institutions that are working with Intel and using Intel artificial intelligence technology to tackle some of the biggest challenges in healthcare.

More: One Simple Truth about Artificial Intelligence in Healthcare: It’s Already Here (Navin Shenoy Editorial) | Shaping the Future of Healthcare through Artificial Intelligence (Event Video Replay) | Artificial Intelligence (Press Kit) | Advancing Data-Driven Healthcare Solutions (Press Kit)

“We are only at the beginning of solving these problems. We are continuing to push forward and work with industry-leading entities to solve even more,” said Arjun Bansal, vice president of the Artificial Intelligence Products Group and general manager of Artificial Intelligence Labs and Software at Intel Corporation. “For example, I’m happy to announce that Kyoto University has recently chosen Intel to power their clinical genome analysis cluster and their molecular simulation cluster. These clusters are to aid in their drug discovery efforts and should help reduce the R&D costs of testing different compounds and accelerate precision medicine by adopting Deep Learning techniques.”

It can take up to 15 years – and billions of dollars – to translate a drug discovery idea from initial inception to a market-ready product. Identifying the right protein to manipulate in a disease, proving the concept, optimizing the molecule for delivery to the patient, carrying out pre-clinical and clinical safety and efficacy testing are all essential, but ultimately the process takes far too long today.

Dramatic shifts are needed to meet the needs of society and a future generation of patients. Artificial intelligence presents researchers with an opportunity to do R&D differently – driving down the resources and costs to develop drugs and bringing the potential for a substantial increase in new treatments for serious diseases.

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