Sometimes people ask me advice about academia. I have no idea what I'm doing 80% of the time. All my PI proposals in last 2 years have been rejected. In my entire academic career, I've never been able to get a paper reviewed at JACS, Nat Chem, PNAS, etc. 1/2
I've put together a few of my favorite discussions on the details of doing molecular dynamics. I'll add more as they come. Hopefully they're useful to you! 🧵1/n
1/ FED's NEW PAPER ON STABLECOINS today deserves both praise & a response. It contains something BIG (1st time I've seen the Fed say this🚨🔥) but it misses things too. Wonky topic: how #stablecoins fit into plumbing of #tradfi, which is right up my alley. https://t.co/zBz6t6vssz
The cyclin triggering the cell cycle is not doing what you had assumed it was doing since forever. Mardo Koivomagi discovered it, and after about 6 years of work, this gem is published. This is the most surprising result to come out of my group by far 1/
https://t.co/Chcx6N37yF
New @ChemRxiv preprint: Prediction of Protein pKa with Representation Learning. Here we used *learned molecular representation* from ANI #neuralnetwork potential to build accurate #MachineLearning model for protonation state prediction. #compchem
https://t.co/CHhy245UnV
Deep learning has obviously transformed protein structure prediction, but can it do the same for the rest of biology? In a perspective by @sorger_peter and myself out this week in @naturemethods, we begin to try to answer this question: https://t.co/XSi0fkusRv
Last year we presented #AlphaFold v2 which predicts 3D structures of proteins down to atomic accuracy. Today we’re proud to share the methods in @Nature w/open source code. Excited to see the research this enables. More very soon!
https://t.co/6uiV51Xly5
https://t.co/CLo7EKubBT