Intro

This chapter explores the integration of artificial intelligence within biological systems, emphasizing significant challenges and how AI can mitigate them. Through a discussion of recent publications, it highlights innovative machine learning approaches that advance genetics and drug discovery, particularly in the evolution of antibody complexes.

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In this episode of The Cognitive Revolution, Nathan explores the cutting-edge intersection of AI and biology with Stanford assistant professor Brian Hie. Discover how AI is revolutionizing our understanding of biological systems and creating new possibilities for interventions. Brian discusses his groundbreaking papers on hybrid AI architectures, the surprising capabilities of language models trained on DNA sequences, and AI-guided evolution of antibodies. Join us for an insightful journey into the future of AI in biology, touching on biosecurity, drug discovery, and the challenges of interpreting AI models in biological contexts.

Links :

Brian Hie : https://x.com/BrianHie

Scanorama paper

Mechanistic Design and Scaling of Hybrid Architectures

Sequence modeling and design from molecular to genome scale with Evo

Unsupervised evolution of protein and antibody complexes with a structure-informed language model

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CHAPTERS:

(00:00:00) About the Show

(00:00:22) About the Episode

(00:03:22) Introduction and Overview

(00:03:55) AI for Biology: Big Picture

(00:05:49) Challenges in Biology and Drug Discovery

(00:08:36) Scanorama and Single Cell Transcriptomics

(00:10:27) Brian's Research Identity

(00:13:00) Mechanistic Design of Hybrid Architectures

(00:14:52) Evo: DNA Language Model (Part 1)

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