Search Results for author: Matteo Turilli

Found 5 papers, 4 papers with code

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.

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

Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations

1 code implementation3 Jan 2018 Jumana Dakka, Kristof Farkas-Pall, Matteo Turilli, David W Wright, Peter V Coveney, Shantenu Jha

This paper makes three main contributions: (1) shows the importance of adaptive execution for ensemble-based free energy protocols to improve binding affinity accuracy; (2) presents and characterizes HTBAC -- a software system that enables the scalable and adaptive execution of binding affinity protocols at scale; and (3) for a widely used free-energy protocol (TIES), shows improvements in the accuracy of simulations for a fixed amount of resource, or reduced resource consumption for a fixed accuracy as a consequence of adaptive execution.

Distributed, Parallel, and Cluster Computing

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