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Deep Surrogate Docking: Accelerating Automated Drug Discovery with Graph Neural Networks
The process of screening molecules for desirable properties is a key step in several applications, ranging from drug discovery to material design.
Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications
At a high level, we conclude that on NVIDIA systems: (1) confounding bottlenecks such as memory inefficiency often dominate runtime costs moreso than data sparsity alone, (2) native Pytorch operations are often as or more competitive than their Pytorch Geometric equivalents, especially at low to moderate levels of input data sparsity, and (3) many operations central to state-of-the-art GNN architectures have little to no optimization for sparsity.
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