Maria Deutscher@AI ? SiliconANGLE
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Isomorphic Labs, an Alphabet spinout focused on AI-driven drug design, has secured $600 million in its first external funding round. The investment, led by Thrive Capital with participation from Alphabet and GV, will fuel the advancement of Isomorphic Labs' AI drug design engine and therapeutic programs. The company aims to leverage artificial intelligence, including its AlphaFold technology, to revolutionize drug discovery across various therapeutic areas, including oncology and immunology. This funding is expected to accelerate research and development efforts, as well as facilitate the expansion of Isomorphic Labs' team with top-tier talent.
Isomorphic Labs, founded in 2021 by Sir Demis Hassabis, seeks to reimagine and accelerate drug discovery by applying AI. Its AI-powered engine streamlines the design of small molecules with therapeutic applications and can predict the effectiveness of a small molecule's attachment to a protein. The company's software also eases other aspects of the drug development workflow. Isomorphic Labs has already established collaborations with pharmaceutical companies like Eli Lilly and Novartis, and the new funding will support the progression of its own drug programs into clinical development. References :
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Sam Khosroshahi@lambdalabs.com
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NVIDIA is pushing the boundaries of artificial intelligence in healthcare and robotics, introducing several groundbreaking advancements. One notable innovation is the DNA LLM, designed to decode the complex genetic information found in DNA, RNA, and proteins. This tool aims to transform genomic research, potentially leading to new understandings and treatments for various diseases.
The company's commitment to AI extends to robotics with the release of Isaac GR00T N1, an open-source platform for humanoid robots. This initiative is expected to accelerate innovation in the field, providing developers with the resources needed to create more advanced and capable robots. Additionally, an NVIDIA research team has developed Hymba, a family of small language models that combine transformer attention with state space models, surpassing the Llama-3.2-3B model in performance while significantly reducing cache size and increasing throughput. References :
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