D
Debsindhu Bhowmik
Researcher at Oak Ridge National Laboratory
Publications - 65
Citations - 1777
Debsindhu Bhowmik is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Neutron scattering & Deep learning. The author has an hindex of 16, co-authored 59 publications receiving 830 citations. Previous affiliations of Debsindhu Bhowmik include Jadavpur University & Wayne State University.
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ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Deep Learning and High Performance Computing
Ahmed Elnaggar,Michael Heinzinger,Christian Dallago,Ghalia Rihawi,Yu Wang,Llion Jones,Tom Gibbs,Tamas Feher,Christoph Angerer,Debsindhu Bhowmik,Burkhard Rost +10 more
TL;DR: In this paper, the authors trained two auto-regressive language models (Transformer-XL and XLNet) on 80 billion amino acids from 200 million protein sequences (UniRef100) and one auto-encoder model on 393 billion amino acid from 2.1 billion protein sequences taken from the Big Fat Database (BFD).
Journal ArticleDOI
ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing
Ahmed Elnaggar,Michael Heinzinger,Christian Dallago,Ghalia Rehawi,Wang Yu,Llion Jones,Tom Gibbs,Tamas Feher,Christoph Angerer,Martin Steinegger,Debsindhu Bhowmik,Burkhard Rost +11 more
TL;DR: In this paper, the authors 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.
Posted ContentDOI
ProtTrans: Towards Cracking the Language of Life’s Code Through Self-Supervised Learning
Ahmed Elnaggar,Michael Heinzinger,Christian Dallago,Ghalia Rehawi,Yu Wang,Llion Jones,Tom Gibbs,Tamas Feher,Christoph Angerer,Martin Steinegger,Debsindhu Bhowmik,Burkhard Rost +11 more
TL;DR: In this paper, the authors 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.
Journal ArticleDOI
Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.
Atanu Acharya,Rupesh Agarwal,Rupesh Agarwal,Matthew B. Baker,Jerome Baudry,Debsindhu Bhowmik,Swen Boehm,Kendall G. Byler,Samuel Yen-Chi Chen,Leighton Coates,Connor J. Cooper,Connor J. Cooper,Omar Demerdash,Isabella Daidone,John D. Eblen,John D. Eblen,Sally R. Ellingson,Stefano Forli,Jens Glaser,James C. Gumbart,John A. Gunnels,Oscar Hernandez,Stephan Irle,Stephan Irle,Daniel W. Kneller,Andrey Kovalevsky,Jeffrey M. Larkin,Travis J Lawrence,Scott LeGrand,Shih-Hsien Liu,Shih-Hsien Liu,Julie C. Mitchell,Gilchan Park,Jerry M. Parks,Jerry M. Parks,Anna Pavlova,Loukas Petridis,Loukas Petridis,Duncan Poole,Line Pouchard,Arvind Ramanathan,David M. Rogers,Diogo Santos-Martins,Aaron Scheinberg,Ada Sedova,Y. Shen,Y. Shen,Jeremy C. Smith,Jeremy C. Smith,Micholas Dean Smith,Micholas Dean Smith,Carlos Soto,A. Tsaris,Mathialakan Thavappiragasam,Andreas F. Tillack,Josh V. Vermaas,V. Q. Vuong,V. Q. Vuong,Junqi Yin,Shinjae Yoo,Mai Zahran,Laura Zanetti-Polzi +61 more
TL;DR: A supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking is presented, including the use of quantum mechanical, machine learning, and artificial intelligence methods to cluster MD trajectories and rescore docking poses.
Journal ArticleDOI
Deep clustering of protein folding simulations.
TL;DR: It is shown that the CVAE model can quantitatively describe complex biophysical processes such as protein folding, and can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features.