The long-term goal of my research is to deepen our understanding of the fundamental biomolecular sequence-structure-function relationship through the lens of computational modeling. My current research takes a critical step toward this goal by developing, evaluating, and disseminating a new generation of knowledge-guided and integrative machine learning methods and resources for modeling and characterization of biomolecules at scale.
I am a recipient of the NSF CAREER Award (2020), NIH MIRA (R35) Award from NIGMS (2020), AU Ginn Faculty Fellowship (2020), AU Outstanding Engineering Faculty Award (2019), and MU Outstanding CS Ph.D. Student Award (2014).
Before joining Virginia Tech in 2022, I was an Assistant Professor at Auburn University from 2017 to 2021. I obtained my Ph.D. in Computer Science from the University of Missouri - Columbia in 2016 under the supervision of Prof. Jianlin Cheng.
1160 Torgersen Hall
620 Drillfield Dr.
Blacksburg, VA 24061
Office Location:
3120B Torgersen Hall
Phone: (540) 231-2865
Fax: (540) 231-6075
Email: dbhattacharya@vt.edu
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30 Dec. 2024:
Announcements
I am always looking for self-motivated Ph.D. students. If you are interested, please apply through the CS at VT Ph.D. admissions process while mentioning my name in your application package and send me an email at dbhattacharya@vt.edu.
Important Note: If you are interested in working in my research group, the best thing to do is to read some of our recently published papers and preprints to get a sense of our research and figure out mutual interest. Please understand that I receive a lot of emails, and may not be able to respond to inquiries that do not exhibit a clear match.
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EquiPNAS: improved protein-nucleic acid binding site prediction using protein-language-model-informed equivariant deep graph neural networks
Rahmatullah Roche, Bernard Moussad, Md Hossain Shuvo, Sumit Tarafder, Debswapna Bhattacharya
Nucleic Acids Research, 52 (5), e27, 2024
Abstract • PDF • Code
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lociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures
Sumit Tarafder*, Debswapna Bhattacharya
Journal of Chemical Information and Modeling, 64 (22), 8655-8664, 2024
Abstract • PDF • Code
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The landscape of RNA 3D structure modeling with transformer networks
Sumit Tarafder*, Rahmatullah Roche*, Debswapna Bhattacharya
Biology Methods and Protocols, 9 (1), bpae047, 2024
Abstract • PDF • Data
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E(3) equivariant graph neural networks for robust and accurate protein-protein interaction site prediction
Rahmatullah Roche, Bernard Moussad, Md Hossain Shuvo, Debswapna Bhattacharya
PLOS Computational Biology, 19(8): e1011435, 2023
Abstract • PDF • Code
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The transformative power of transformers in protein structure prediction
Bernard Moussad, Rahmatullah Roche, Debswapna Bhattacharya
Proceedings of the National Academy of Sciences, 120 (32) e2303499120, 2023
Abstract • PDF • Data
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DisCovER: distance- and orientation-based covariational threading for weakly homologous proteins
Sutanu Bhattacharya, Rahmatullah Roche, Bernard Moussad, Debswapna Bhattacharya
PROTEINS: Structure, Function, and Bioinformatics, 90(2): 579-588, 2022
Abstract • PDF • Code
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QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks
Md Hossain Shuvo, Sutanu Bhattacharya, Debswapna Bhattacharya
Intelligent Systems for Molecular Biology (ISMB), 2020
Bioinformatics, 36(S1): i285-i291, 2020
Abstract • PDF • Code
© Debswapna Bhattacharya,