Super excited to preprint our work on developing a Biomolecular Emulator (BioEmu): Scalable emulation of protein equilibrium ensembles with generative deep learning from @MSFTResearch AI for Science.
#ML#AI#NeuralNetworks#Biology#AI4Science
https://t.co/yzOy6tAoPv
@FrankNoeBerlin@MSFTResearch But the general question of can we capture most of re-organization dGs with a backbone rep is super interesting, esp if it’s a learnable quantity
@FrankNoeBerlin@MSFTResearch There’s a couple of datasets/papers on re-org free energies from Schrödinger: T4 lysozyme (Lim et al), Abl, HSP90 (Fajer et al.)
https://t.co/kE2az4zoIB
https://t.co/u2iZWY7YME
The point of a startup is to make usable technology for others. When you make software, you have to watch at least 10 people use it. Sit next to them and say absolutely nothing. Force yourself to marinate in the failure of your product design.
Every version 1 of any software will be absolutely destroyed by first interaction with users. You need to watch your new creation be absolutely misunderstood by users to reform version 1 into the one that actually works.
There is only one path: figuring out where the sharp edges, the places people get caught, the assumptions you make as a builder that turn out to be wrong, and then relentlessly sanding it down so that anyone can use it.
That is good design. That is the key to a good product. There is no shortcut for this.
WATCH USERS AND CRINGE AND THEN FIX IT.
SAND DOWN THE EDGES.
It is turning out to not be the case.
"Structure=>function" had seemed easy because there were all these papers solving a key protein's structure on the way to understanding what it does: "ah, these residues here make a pocket and that one moves, and it all makes sense..." 12/