Search Results for author: Arvind Ramanathan

Found 11 papers, 5 papers with code

Protein Folding Neural Networks Are Not Robust

no code implementations9 Sep 2021 Sumit Kumar Jha, Arvind Ramanathan, Rickard Ewetz, Alvaro Velasquez, Susmit Jha

We define the robustness measure for the predicted structure of a protein sequence to be the inverse of the root-mean-square distance (RMSD) in the predicted structure and the structure of its adversarially perturbed sequence.

Adversarial Attack Protein Folding

CrossedWires: A Dataset of Syntactically Equivalent but Semantically Disparate Deep Learning Models

1 code implementation29 Aug 2021 Max Zvyagin, Thomas Brettin, Arvind Ramanathan, Sumit Kumar Jha

Currently, our ability to build standardized deep learning models is limited by the availability of a suite of neural network and corresponding training hyperparameter benchmarks that expose differences between existing deep learning frameworks.

Hyperparameter Optimization

Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening

1 code implementation13 Jun 2021 Austin Clyde, Thomas Brettin, Alexander Partin, Hyunseung Yoo, Yadu Babuji, Ben Blaiszik, Andre Merzky, Matteo Turilli, Shantenu Jha, Arvind Ramanathan, Rick Stevens

Our analysis of the speedup explains that to screen more molecules under a docking paradigm, another order of magnitude speedup must come from model accuracy rather than computing speed (which, if increased, will not anymore alter our throughput to screen molecules).

Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations

no code implementations10 Apr 2021 Alexander Brace, Igor Yakushin, Heng Ma, Anda Trifan, Todd Munson, Ian Foster, Arvind Ramanathan, Hyungro Lee, Matteo Turilli, Shantenu Jha

The use of ML methods to dynamically steer ensemble-based simulations promises significant improvements in the performance of scientific applications.

Scaffold Embeddings: Learning the Structure Spanned by Chemical Fragments, Scaffolds and Compounds

no code implementations11 Mar 2021 Austin Clyde, Arvind Ramanathan, Rick Stevens

Molecules have seemed like a natural fit to deep learning's tendency to handle a complex structure through representation learning, given enough data.

Drug Discovery Representation Learning

Artificial intelligence techniques for integrative structural biology of intrinsically disordered proteins

no code implementations1 Dec 2020 Arvind Ramanathan, Heng Ma, Akash Parvatikar, Chakra S. Chennubhotla

We outline recent developments in artificial intelligence (AI) and machine learning (ML) techniques for integrative structural biology of intrinsically disordered proteins (IDP) ensembles.

Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release

1 code implementation28 May 2020 Yadu Babuji, Ben Blaiszik, Tom Brettin, Kyle Chard, Ryan Chard, Austin Clyde, Ian Foster, Zhi Hong, Shantenu Jha, Zhuozhao Li, Xuefeng Liu, Arvind Ramanathan, Yi Ren, Nicholaus Saint, Marcus Schwarting, Rick Stevens, Hubertus van Dam, Rick Wagner

Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding

1 code implementation17 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.

Protein Folding

Hierarchical Convolutional Attention Networks for Text Classification

no code implementations WS 2018 Shang Gao, Arvind Ramanathan, Georgia Tourassi

Recent work in machine translation has demonstrated that self-attention mechanisms can be used in place of recurrent neural networks to increase training speed without sacrificing model accuracy.

Classification Document Classification +4

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