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Property Prediction

Property prediction involves forecasting or estimating a molecule's inherent physical and chemical properties based on information derived from its structural characteristics. It facilitates high-throughput evaluation of an extensive array of molecular properties, enabling the virtual screening of compounds. Additionally, it provides the means to predict the unknown attributes of new molecules, thereby bolstering research efficiency and reducing development times.

Papers

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Showing 581–590 of 691 papers

TitleStatusHype
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision—0
Equivariant Graph Attention Networks for Molecular Property Prediction—0
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer—0
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN)—0
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events—0
Formula graph self-attention network for representation-domain independent materials discoveryCode0
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy—0
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation—0
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation LearningCode0
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing—0
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