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GTC ON-DEMAND

Computational Biology & Chemistry
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Using GPUs to Generate Reproducible Workflows to Accelerate Drug Discovery
Abstract:
The existing drug discovery process is costly, slow, and in need of innovation. At ATOM, a public-private consortium consisting of LLNL, GSK, UCSF, and FNL, we built an HPC-driven drug discovery pipeline that is supported by GPU-enabled supercomputers and containerized infrastructure. We'll describe the pipeline's infrastructure, including our data lake and model zoo, and share lessons learned along the way. We'll discuss the data-driven modeling pipeline we're using to create thousands of optimized models and the critical role of GPUs in this work. We'll also share model performance results and touch on how these models are integral to ATOM's new drug discovery paradigm. By building GPU-Accelerated tools, we aim to transform drug discovery from a time-consuming and sequential process to a highly parallelized and integrated approach.
 
Topics:
Computational Biology & Chemistry, AI & Deep Learning Research
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9950
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