1 citations · 5 across the 10 of their papers we have counts for
5 papers · 1 filter
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry
Hatem Helal, Jesun Firoz, Jenna Bilbrey +5
Molecular property calculations are the bedrock of chemical physics. High-fidelity \textit{ab initio} modeling techniques for computing the molecular properties can be prohibitivel…
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators
Jenna A. Bilbrey, Kristina M. Herman, Henry Sprueill +6
The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task…
Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks
Michael Hunter Ashby, Jenna A. Bilbrey
We apply a temporal edge prediction model for weighted dynamic graphs to predict time-dependent changes in molecular structure. Each molecule is represented as a complete graph in…
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
Logan Ward, Jenna A. Bilbrey, Sutanay Choudhury +2
Design of new drug compounds with target properties is a key area of research in generative modeling. We present a small drug molecule design pipeline based on graph-generative mod…
HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data
Sutanay Choudhury, Jenna A. Bilbrey, Logan Ward +5
Intermolecular and long-range interactions are central to phenomena as diverse as gene regulation, topological states of quantum materials, electrolyte transport in batteries, and…