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NVIDIA Makes Drug Discovery, Medical Imaging, and Genomics Easier with Cloud-Based AI

by Fred Pennic 05/03/2024 Leave a Comment

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What You Should Know:

– Harnessing the power of AI for healthcare just got simpler. NVIDIA announced the integration of NVIDIA NIM, a collection of cloud-native AI microservices, with Amazon Web Services (AWS). 

– The strategic collaboration empowers developers to leverage pre-trained AI models for drug discovery, medical imaging analysis, and genomics research – all accessible through user-friendly APIs.

Streamlined Access to Powerful AI Tools

NIM integrates seamlessly with popular AWS services like SageMaker (for machine learning model development) and ParallelCluster (for high-performance computing). Additionally, AWS HealthOmics, a specialized service for biological data analysis, can orchestrate NIM workflows. This integration simplifies the process of deploying cutting-edge AI models for healthcare and life sciences companies already using the AWS cloud infrastructure.

Benefits of NVIDIA NIM on AWS

  • Faster Development: Researchers can bypass complex model development and packaging, accelerating the deployment of generative AI solutions.
  • Multimodal Workflows: NIM facilitates the creation of workflows combining AI models from various sources, like protein sequences, medical images, and patient records.
  • Enhanced Drug Discovery: BioNeMo, a foundation for AI models in drug discovery, is included in NIM. Companies like Amgen are already utilizing BioNeMo to train protein design models.

Real-World Examples of Success

  • A-Alpha Bio: This company developing protein interaction prediction tools saw a 10x speedup by utilizing BioNeMo models optimized for NVIDIA GPUs on AWS.
  • Agilent: This life sciences leader used NVIDIA Parabricks, a genomics analysis tool within NIM, to significantly improve processing speeds for variant calling workflows.

Beyond Drug Discovery and Genomics

NIM offers more than just scientific applications. It also includes:

  • Large Language Models: These powerful language processing tools can be used to create conversational AI healthcare assistants for patient education and clinician support.
  • Visual Generative AI: This technology allows for the development of digital avatars and chatbots for a more engaging user experience.
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