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Found 18 papers, 9 papers with code
Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge
Understanding protein interactions and pathway knowledge is crucial for unraveling the complexities of living systems and investigating the underlying mechanisms of biological functions and complex diseases.
FAIR for AI: An interdisciplinary and international community building perspective
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
AI-coupled HPC Workflows
Increasingly, scientific discovery requires sophisticated and scalable workflows.
Asynchronous Execution of Heterogeneous Tasks in ML-driven HPC Workflows
Heterogeneous scientific workflows consist of numerous types of tasks that require executing on heterogeneous resources.
Optimal Decision Making in High-Throughput Virtual Screening Pipelines
The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design.
Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening
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
The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity.
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow.
Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release
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).
Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations
We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities.
DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding
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.
Understanding ML driven HPC: Applications and Infrastructure
We recently outlined the vision of "Learning Everywhere" which captures the possibility and impact of how learning methods and traditional HPC methods can be coupled together.
Parallel Performance of Molecular Dynamics Trajectory Analysis
The performance of biomolecular molecular dynamics simulations has steadily increased on modern high performance computing resources but acceleration of the analysis of the output trajectories has lagged behind so that analyzing simulations is becoming a bottleneck.
Distributed, Parallel, and Cluster Computing Quantitative Methods D.1.3; J.2
Task-parallel Analysis of Molecular Dynamics Trajectories
We also provide a quantitative performance analysis of the different algorithms across the three frameworks.
Distributed, Parallel, and Cluster Computing
Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations
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
Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks
Smart systems that can accurately diagnose patients with mental disorders and identify effective treatments based on brain functional imaging data are of great applicability and are gaining much attention.
Ensemble Toolkit: Scalable and Flexible Execution of Ensembles of Tasks
Motivated by the missing capabilities of these computing systems and the increasing importance of task-level parallelism, we introduce the Ensemble toolkit which has the following application development features: (i) abstractions that enable the expression of ensembles as primary entities, and (ii) support for ensemble-based execution patterns that capture the majority of application scenarios.
Distributed, Parallel, and Cluster Computing
Hadoop on HPC: Integrating Hadoop and Pilot-based Dynamic Resource Management
High-performance computing platforms such as supercomputers have traditionally been designed to meet the compute demands of scientific applications.
Distributed, Parallel, and Cluster Computing
