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Found 5 papers, 4 papers with code
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators
The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task.
Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks
These models are further adapted for regression tasks to predict experimental binding affinities and pIC50 is crucial for drugs potency and efficacy.
Geometric learning of the conformational dynamics of molecules using dynamic graph neural networks
We apply a temporal edge prediction model for weighted dynamic graphs to predict time-dependent changes in molecular structure.
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
Design of new drug compounds with target properties is a key area of research in generative modeling.
HydroNet: Benchmark Tasks for Preserving Intermolecular Interactions and Structural Motifs in Predictive and Generative Models for Molecular Data
Intermolecular and long-range interactions are central to phenomena as diverse as gene regulation, topological states of quantum materials, electrolyte transport in batteries, and the universal solvation properties of water.
