Training Data
Arc Institute's Patrick Hsu on Building an App Store for Biology with AI
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Show notes
Patrick Hsu, co-founder of Arc Institute, discusses the opportunities for AI in biology beyond just drug development, and how Evo 2, their new biology foundation model, is enabling a broad ecosystem of applications. Evo 2 was trained on a vast dataset of genomic data to learn evolutionary patterns that would have taken years to find; as a result, the model can be used for applications from identifying mutations that cause disease to designing new molecular and even genome scale biological systems.
Hosted by Josephine Chen and Pat Grady, Sequoia Capital
Mentioned in this episode:
Sequence modeling and design from molecular to genome scale with Evo: Public pre-print of original Evo paper
Genome modeling and design across all domains of life with Evo 2: Public pre-print of Evo 2 paper
ClinVar: NIH database of the genes that are known to cause disease, and mutations in those genes causally associated with disease state
Sequence Read Archive: Massive NIH database of gene sequencing data
Machines of Loving Grace: Daria Amodei essay that Patrick cites on how AI could transform the world for the better
Arc Virtual Cell Atlas: Arc’s first step toward assembling, curating and generating large-scale cellular data from AI-driven biological discovery (among many other tools)
Protein Data Bank (PDB): a global archive of 3D structural information of biomolecules used by DeepMind to train AlphaFold
OpenAI Deep Research: The one AI app Patrick uses daily