Arvind Ramanathan (he/him)

If you are interested in working at the intersection of AI, robotics/automation and biology, come and build the next generation of open source toolkits for scientific discovery w/ me at @argonne . Apply here: bit.ly/4pwYFjS

@BrianHie @Nature @aditimerch Really beautiful paper, @BrianHie ! Congrats!

Today in @Nature, in work led by @aditimerch, we report the ability to prompt Evo to generate functional de novo genes. You shall know a gene by the company it keeps! 1/n

@chaitjo Really cool post @chaitjo!

Beyond Structure-based Biomolecule Design 🧬🚀 Its an important moment: models starting to work, multiple biotechs building similar protein design foundation models, and action shifting from academia to industry. My new substack article: chaitjo.substack.com/p/beyond-struc…

It was a privilege to present at the Caltech UChicago AI + Science meeting following none other than @AnimaAnandkumar and @francesarnold and all the wonderful speakers including @KyleCranmer! Really thrilled to see how AI is impacting so many facets of scientific discovery!

@ferruz_noelia Hi @ferruz_noelia - very interesting work! Have been following a lot of your lab’s work. Our paper on using DPO for proteins design integrating natural language was recently presented at supercomputing conference with @AnimaAnandkumar. computer.org/csdl/proceedin…

Are you at the NeurIPS? Come see my talk tomorrow at the MLSB workshop at 8:30! I'll talk about how we're using reinforcement learning to guide protein language models 🚦🛤️📈 Our preprint seems stuck in the screening process; see our repo for now!: github.com/AI4PDLab/DPO_p…

MProt-DPO: Breaking the ExaFLOPS Barrier for Multimodal Protein Design Workflows with Direct Preference Optimization • This paper presents MProt-DPO, an innovative framework that achieves ExaFLOPS-scale performance for protein design, combining AI and high-performance computing (HPC) to generate and optimize protein sequences across multiple supercomputers. • Key breakthroughs include the integration of Direct Preference Optimization (DPO), allowing fine-tuning of protein language models based on preferred structural and functional characteristics, enhancing model capability to generate “fit” protein variants effectively. • MProt-DPO utilizes multimodal input (sequence, structural, and natural language descriptors), bridging data from simulations and experiments to guide protein design towards desired functional landscapes. • Achieved a record-breaking 4.11 ExaFLOPS sustained performance on Aurora, demonstrating scalability across five supercomputing systems, marking a milestone in protein engineering and HPC synergy. @arvindr_ @AnimaAnandkumar @mpapka @ChaoweiX @Shengchao_Liu @archit_vasan @servesh_m @WardLT2 @argonne 📜Paper: computer.org/csdl/proceedin… #ProteinDesign #HPC #MachineLearning #AI #ComputationalBiology

@anthonygitter Will post it on bioarxiv. Just waiting to get the appropriate "embargo" off of us.

@arvindr_ Is the MProt-DPO paper available anywhere? The link in the article takes me to a page that requires registration.

What's more fun when you can break the Exascale barrier for #protein design workflows with #artificalintelligence, that can be prompted with natural language. Read more here: alcf.anl.gov/news/argonne-t…

The fun part is in scaling these models across five supercomputing platforms including @ALCF's Aurora, @OLCFGOV Frontier, @CINECA's Leonardo and @cscsch Alps machines.

We show that it is possible to minimize hallucinations with such models by integrating experimental or simulation observables allowing the model to sample not only novel sequence space, but cover reliable ground in terms of protein fitness landscapes.

You can register here: cvent.me/LYMMR2

We also have hands on tutorial sections, interactive sessions and lab tours to show how scientific discoveries are being transformed with generative AI.

Interested in knowing about what @argonne is working on at the intersection of AI, robotics and scientific discovery? Look at our program line up for this year's Scientific Discovery in the AI Age meeting at Argonne. acadev.github.io/sdl-workshop-2…

@RommieAmaro @ltchong Thank you! Lots of fun and lots of interesting directions emerging!

👀 nice work @ltchong @arvindr_

Check out some recent work integrating MD simulations with rare event sampling and DeepDriveMD with the wonderful @ltchong and our group. Really enjoyed the research, discussions, and the directions this work is taking!

Please join @icmlconf for "Equivariant Graph Neural Operator for Modeling 3D Dynamics" by @MinkaiX, @jiaqihan99, @aaron_lou, @JeanKossaifi,@arvindr_, @jure, @StefanoErmon, and @AnimaAnandkumar Wed 24 Jul 1:30 p.m. CEST — 3 p.m. CEST Hall C 4-9 #203 x.com/AnimaAnandkuma…

@shozeb_haider @chapelhillgov What are you doing in chapel hill?

Not every day you see multilingual signs featuring Burmese. But here in the town of Chapel Hill you do. @chapelhillgov