1 code implementation • 17 Sep 2019 • Hyungro Lee, Heng Ma, Matteo Turilli, Debsindhu Bhowmik, Shantenu Jha, Arvind Ramanathan
Our study provides a quantitative basis to understand how DL driven MD simulations, can lead to effective performance gains and reduced times to solution on supercomputing resources.
1 code implementation • 13 Jul 2020 • Ahmed Elnaggar, Michael Heinzinger, Christian Dallago, Ghalia Rihawi, Yu Wang, Llion Jones, Tom Gibbs, Tamas Feher, Christoph Angerer, Martin Steinegger, Debsindhu Bhowmik, Burkhard Rost
Here, we trained two auto-regressive models (Transformer-XL, XLNet) and four auto-encoder models (BERT, Albert, Electra, T5) on data from UniRef and BFD containing up to 393 billion amino acids.
Ranked #1 on Protein Secondary Structure Prediction on CASP12
Dimensionality Reduction Protein Secondary Structure Prediction
no code implementations • 25 Oct 2023 • Pei Zhang, Logan Kearney, Debsindhu Bhowmik, Zachary Fox, Amit K. Naskar, John Gounley
Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and materials discovery.