Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us
About this episode
From the show’s notesa16z Journal Club (part of the a16z Podcast), curates and covers recent advances from the scientific literature -- what papers we’re reading, and why they matter from our perspective at the intersection of biology & technology (for bio journal club). This inaugural episode covers 2 different topics, in discussion with Lauren Richardson:
0:26 #1 identifying new antibiotics through a novel machine-learning based approach -- a16z general partner Vijay Pande and bio deal partner Andy Tran discuss the business of pharma; the specific methods/ how it works; and other applications for deep learning in drug discovery and development based on this paper:
Read the show’s notes in full
"A Deep Learning Approach to Antibiotic Discovery" in Cell (February 2020), by Jonathan Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina Donghia, Craig MacNair, Shawn French, Lindsey Carfrae, Zohar Bloom-Ackermann, Victoria Tran, Anush Chiappino-Pepe, Ahmed Badran, Ian Andrews, Emma Chory, George Church, Eric Brown, Tommi Jaakkola, Regina Barzilay, James Collins
11:43 #2 characterizing the novel coronavirus causing the COVID-19 pandemic -- a16z bio deal partner Judy Savitskaya shares what we can learn from the protein structures; the relationship to the 2002-2004 SARS epidemic; and more based on these two research articles:
"Structure, Function, and Antigenicity of the SARS-CoV-2 Spike Glycoprotein" in Cell (April 2020), by Alexandra Walls, Young-Jun Park, M. Tortorici, Abigail Wall, Andrew McGuire, David Veesler
"Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation" in Science (March 2020), by Daniel Wrapp, Nianshuang Wang, Kizzmekia Corbett, Jory Goldsmith, Ching-Lin Hsieh, Olubukola Abiona, Barney Graham, Jason McLellan
You can find these episodes at a16z.com/journalclub.
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