Journal Club: Finding New Antibiotics with Machine Learning, What Coronavirus Structures Tell Us

a16z 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:

• "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.