Ramith Hettiarachchi

@ramith.fyi

PhD Student @CMUPittCompBio.bsky.social / @SCSatCMU.bsky.social Interested in ML for science/Compuational drug discovery/AI-assisted scientific discovery 🤞 from 🇱🇰🫶 https://new.ramith.fyi

been building an architecture explainer/explorer tool where you can go in varying depth of abstraction to understand: - started with the genie3 (in progress), then go towards AF2 Underneath, it's a compiler for turning model architecture descriptions into interactive diagrams scope.new.ramith.fyi

Glad to have worked with this amazing team in organizing the GenBio workshop! ❤️ (There’s lot more who helped behind the scenes and couldn’t join in this picture) GenBio has been a great venue to bring researchers and foster very interesting convos.. wish I had more time to talk to even more ppl 😆

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Registration line 😅 even after two days of registration.. ml conferences maybe reaching a breaking point 😆 i guess it makes more sense to have more decentralized events

So ESMFold2 uses 3DRoPE to enforce a geometry like pairbias (without the mem complexity of the standard pairbias) in its atom transformer… Wonder if the next stage, token transformer could’ve survived a similar approach (rather than the ln proj of z)

Glad to have contributed to this study on the computational side.. 😃 Thanks to implosion carving, we can now build devices that operate in the visible wavelength regime for classification tasks. Machine learning driven physical device design is pretty exciting.. news.mit.edu/2026/powerfu...

Powerful shrinking technique could enable devices that compute with light

MIT researchers made 3D devices with nanoscale features that can perform optical computing tasks using visible light. They developed a technique that creates vacancies throughout a hydrogel, then shri...

news.mit.edu

Equivariance is dead! 😢 Or is it? 😈 Genie 3 is out! Our latest protein design model achieves SoTA results for binder design and motif scaffolding, greatly improving on BindCraft and Proteina-Complexa. It does so using all-atom SE(3)-equivariance based on a branched polymer representation👇

Yeqing Lin@yeqinglin.bsky.social · 4mo ago

Introducing Genie 3, a generative protein model that substantially advances the state-of-the-art for binder design, increasing in silico success rates by up to 20x on hard multimeric targets. It also debuts a form of inference-time scaling unobserved in other design models. 🧵1/8

La proteina’s autoencoder can be thought of as a inverse folder to some degree 🤔 Wonder if just giving Ca, along with a random partial latent acts as a inverse folder (true we get the all atom structure as well)

noticing that backbone design models trained on AFDB 588K, when sampled through ODE results in poor designability (~25%). But through a proper noise schedule in SDE it goes to 90% (as reported in lit.) this felt counterintuitive, wonder if the reason is, AFDB being just 42% designable to begin with

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