{"id":741657,"date":"2023-03-21T11:53:22","date_gmt":"2023-03-21T15:53:22","guid":{"rendered":"https:\/\/www.marketnewsdesk.com\/index.php\/nvidia-unveils-large-language-models-and-generative-ai-service-to-advance-life-sciences-rd\/"},"modified":"2023-03-21T11:53:22","modified_gmt":"2023-03-21T15:53:22","slug":"nvidia-unveils-large-language-models-and-generative-ai-service-to-advance-life-sciences-rd","status":"publish","type":"post","link":"https:\/\/www.marketnewsdesk.com\/index.php\/nvidia-unveils-large-language-models-and-generative-ai-service-to-advance-life-sciences-rd\/","title":{"rendered":"NVIDIA Unveils Large Language Models and Generative AI Service to Advance Life Sciences R&D"},"content":{"rendered":"

\nPart of NVIDIA AI Foundations, New BioNeMo Cloud Service Accelerates Life Sciences Research, Drug Discovery and Protein Engineering; Amgen and a Dozen Startups Among Early Access Customers
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SANTA CLARA, Calif., March 21, 2023 (GLOBE NEWSWIRE) — GTC<\/strong>\u00a0—\u00a0NVIDIA today announced an expanded set of generative AI cloud services for customizing AI foundation models to accelerate the creation of new proteins and therapeutics, as well as research in the fields of genomics, chemistry, biology and molecular dynamics.<\/p>\n

Part of NVIDIA AI Foundations<\/u><\/a>, the new BioNeMo\u2122 Cloud service<\/u><\/a> offering \u2014 for both AI model training and inference \u2014 accelerates the most time-consuming and costly stages of drug discovery. It enables researchers to fine-tune generative AI applications on their own proprietary data, and to run AI model inference directly in a web browser or through new cloud application programming interfaces (APIs) that easily integrate into existing applications.<\/p>\n

\u201cThe transformative power of generative AI holds enormous promise for the life science and pharmaceutical industries,\u201d said Kimberly Powell, vice president of healthcare at NVIDIA. \u201cNVIDIA\u2019s long collaboration with pioneers in the field has led to the development of BioNeMo Cloud Service, which is already serving as an AI drug discovery laboratory. It provides pretrained models and allows customization of models with proprietary data that serve every stage of the drug-discovery pipeline, helping researchers identify the right target, design molecules and proteins, and predict their interactions in the body to develop the best drug candidate.\u201d<\/p>\n

\n Amgen Among Early Users<\/strong>
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Amgen, one of the world\u2019s leading biotechnology companies, is already using the service to advance its research and development efforts.<\/p>\n

\u201cBioNeMo is dramatically accelerating our approach to biologics discovery,\u201d said Peter Grandsard, executive director of Biologics Therapeutic Discovery, Center for Research Acceleration by Digital Innovation at Amgen. \u201cWith it, we can pretrain large language models<\/u><\/a> for molecular biology on Amgen\u2019s proprietary data, enabling us to explore and develop therapeutic proteins for the next generation of medicine that will help patients.\u201d<\/p>\n

\n Generative AI Supercharges Drug Discovery Pipeline <\/strong>
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BioNeMo Cloud service includes pretrained AI models<\/a> to help researchers build AI pipelines for drug development. It has been adopted by drug-discovery companies including Evozyne and Insilico Medicine to support data-driven drug design for new therapeutic candidates.<\/p>\n

Generative AI models can rapidly identify potential drug molecules \u2014 in some cases designing compounds or protein-based therapeutics from scratch. Trained on large-scale datasets of small molecules, proteins, DNA and RNA sequences, these models can predict the 3D structure of a protein and how well a molecule will dock with a target protein.<\/p>\n

\n New Generative AI Models Available With BioNeMo Service Early Access<\/strong>
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BioNeMo now has six new optimized, open-source models<\/u><\/a>, in addition to its previously announced MegaMolBART generative chemistry model, ESM1nv protein language model and OpenFold protein structure prediction model. They include:<\/p>\n