Prospects for Generalizable Foundation Models

Sponsor note: I am extremely happy to announce my first commercial, service-oriented sponsor: rush.cloud. I’ve been doing these podcasts entirely through very kind philanthropic graces, which is very nice, but I’d ideally like to be helping someone when they sponsor me. And now I have that! So, if you are at all involved doing preclinical drug discovery and would benefit from computational tools, you should check out their platform + beautiful website here: rush.cloud.

******

Youtube: https://www.youtube.com/watch?v=W0m3Ltz_YqU

Apple Podcasts: https://podcasts.apple.com/us/podcast/owl-posting/id1758545538?i=1000736122646

Spotify: https://open.spotify.com/episode/5l9RMbMwdgOrrZ6uLS656R?si=938af7d2b79440a1

Transcript: https://www.owlposting.com/p/can-machine-learning-enable-100-plex?open=false#%C2%A7transcript

******

Introduction:

Ellen Zhong is perhaps one of the only people in the ML x bio field to have created an entirely new subfield of research during her PhD: the application of deep-learning to cryo-EM particle images.

If you aren’t familiar with that field, I luckily have a 8,000~ word article covering it, which walks through a lot of Ellen’s papers. If you don’t have time to read something that grossly large, the general breakdown of the problem is as follows: cryo-EM can give you thousands of 2D views of a 3D protein from many different angles, from that data, can you discover what that 3D structure is? Ellen, who is a computer science professor at Princeton University, has spent her academic career investigating that question, and now has an entire lab at Princeton (E.Z. Lab) focused on that and related ones. Including, as the title mentions, the possibility of doing performing cryo-EM structure determination at ultra-high scales.

In this podcast, we talk about her research, what she did during her recent sabbatical at Generate:Biomedicines, her recent interest in areas beyond cryo-EM (cryo-ET and NMR specifically), and more!

Timestamps

[00:00:00] Introduction

[00:02:43]  What does it mean to apply ML to cryo-EM?

[00:04:28] Ab initio reconstruction and conformational heterogeneity

[00:15:41] Can we do multiplex cryo-EM structure determination?

[00:22:19] Datasets in cryo-EM

[00:26:25] Why isn’t there a foundation model for cryo-EM particle analysis?

[00:33:07]  How much practical usage is there of these cryo-EM models amongst wet-lab cryo-EM researchers?

[00:40:34] Where can things still improve?

[00:46:57]  Has deep learning done something in cryo-EM that was previously impossible?

[00:48:22] Ellen’s experience in the cryo-EM field

[00:53:40] Deep learning in cryo-EM outside of structure determination

[00:57:32] 3D volume reconstruction versus residue assignment in cryo-EM

[01:00:26] What did Ellen do during her sabbatical at Generate Biomedicines?

[01:07:07] Ellen’s research in cryo-ET

[01:13:54] Ellen’s research in NMR

[01:21:05] How did Ellen get into the cryo-EM field?

[01:26:57] Why did Ellen go back to graduate school?

[01:32:17]  What makes Ellen more confident about trusting an external cryo-EM paper?