Arvind Ramanathan (he/him)

Pasta and Protein. Music and Mysteries. Life and Lessons. Computational biologist- sometimes at work :)

[Arvind Ramanathan (he/him) @arvindr_]

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: https://t.co/3Eg2Gf2KyQ

[Arvind Ramanathan (he/him) @arvindr_]

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

[Arvind Ramanathan (he/him) @arvindr_]

@chaitjo Really cool post @chaitjo!

[Arvind Ramanathan (he/him) @arvindr_]

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!

[Prof. Anima Anandkumar @AnimaAnandkumar]

@francesarnold Arvind talking about generative models and agents for biology

[Arvind Ramanathan (he/him) @arvindr_]

@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. https://t.co/GCJLM4B2rc

[Biology+AI Daily @BiologyAIDaily]

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: https://t.co/ykOoJ2UXO7

#ProteinDesign #HPC #MachineLearning #AI #ComputationalBiology

[Arvind Ramanathan (he/him) @arvindr_]

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

[Arvind Ramanathan (he/him) @arvindr_]

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: https://t.co/8Ta3JANWgS

[Arvind Ramanathan (he/him) @arvindr_]

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.

[Arvind Ramanathan (he/him) @arvindr_]

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.

[Arvind Ramanathan (he/him) @arvindr_]

You can register here: https://t.co/rcNL6aHblg

[Arvind Ramanathan (he/him) @arvindr_]

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. https://t.co/z7kkP4SRiN

[Arvind Ramanathan (he/him) @arvindr_]

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

[Arvind Ramanathan (he/him) @arvindr_]

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

[Arvind Ramanathan (he/him) @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!

[Leo Zang @LeoTZ03]

Unsupervised learning of progress coordinates during weighted ensemble simulations: Application to millisecond protein folding
- Improve rare events in protein folding (e.g., state transitions) through weighted ensemble simulation and an unsupervised deep learning model.
- Use a convolutional VAE to compress contact maps into latent space, and applies a Local Outlier Factor to identify outlier conformations, which are then replicated in the simulation.
- Training the CVAE on-the-fly works better than using a pretrained model.
Preprint: https://t.co/gcKKFubk2P

[Kamyar Azizzadenesheli @Azizzadenesheli]

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

https://t.co/aIziGJFQIB

[Arvind Ramanathan (he/him) @arvindr_]

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