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Siddharth Viswanath

Alumni

Publications

ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics
Siddharth Viswanath
Dhananjay Bhaskar
David R. Johnson
João F. Rocha
Egbert Castro
Jackson Grady
Alex T. Grigas
Michael Perlmutter
Corey S. O'Hern
Smita Krishnaswamy
Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress… (voir plus) has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations.
2024-10-27
ArXiv (prépublication)
doi.org
arxiv.org
Convergence of Manifold Filter-Combine Networks
David R. Johnson
Joyce Chew
Siddharth Viswanath
Edward De Brouwer
Deanna Needell
Smita Krishnaswamy
Michael Perlmutter
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). The filter-combine fra… (voir plus)mework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and naturally suggests many interesting families of MNNs which can be interpreted as the manifold analog of various popular GNNs. We then propose a method for implementing MFCNs on high-dimensional point clouds that relies on approximating the manifold by a sparse graph. We prove that our method is consistent in the sense that it converges to a continuum limit as the number of data points tends to infinity.
2024-10-18
ArXiv (prépublication)
doi.org
arxiv.org
Convergence of Manifold Filter-Combine Networks
David R. Johnson
Joyce A. Chew
Siddharth Viswanath
Edward De Brouwer
Deanna Needell
Smita Krishnaswamy
Michael Perlmutter
In order to better understand manifold neural networks (MNNs), we introduce Manifold Filter-Combine Networks (MFCNs). The filter-combine fra… (voir plus)mework parallels the popular aggregate-combine paradigm for graph neural networks (GNNs) and naturally suggests many interesting families of MNNs which can be interpreted as the manifold analog of various popular GNNs. We then propose a method for implementing MFCNs on high-dimensional point clouds that relies on approximating the manifold by a sparse graph. We prove that our method is consistent in the sense that it converges to a continuum limit as the number of data points tends to infinity.
2024-10-18
ArXiv (prépublication)
doi.org
arxiv.org
ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics
Siddharth Viswanath
Dhananjay Bhaskar
David R. Johnson
João Felipe Rocha
Egbert Castro
Jackson Grady
Alex T. Grigas
Michael Perlmutter
Corey S. O'Hern
Smita Krishnaswamy
Understanding the dynamic nature of protein structures is essential for comprehending their biological functions. While significant progress… (voir plus) has been made in predicting static folded structures, modeling protein motions on microsecond to millisecond scales remains challenging. To address these challenges, we introduce a novel deep learning architecture, Protein Transformer with Scattering, Attention, and Positional Embedding (ProtSCAPE), which leverages the geometric scattering transform alongside transformer-based attention mechanisms to capture protein dynamics from molecular dynamics (MD) simulations. ProtSCAPE utilizes the multi-scale nature of the geometric scattering transform to extract features from protein structures conceptualized as graphs and integrates these features with dual attention structures that focus on residues and amino acid signals, generating latent representations of protein trajectories. Furthermore, ProtSCAPE incorporates a regression head to enforce temporally coherent latent representations.
2024-10-01
arXiv (publié)
doi.org
arxiv.org
Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning
Holly Steach
Siddharth Viswanath
Yixuan He
Xitong Zhang
Natalia Ivanova
Matthew Hirn
Michael Perlmutter
Smita Krishnaswamy
2024-05-17
Lecture Notes in Computer Science (publié)
doi.org