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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 631–640 of 691 papers

TitleStatusHype
Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems—0
Object-Centric Learning with Slot AttentionCode1
A Multiscale Graph Convolutional Network Using Hierarchical Clustering—0
Self-Supervised Graph Transformer on Large-Scale Molecular DataCode1
Wasserstein Embedding for Graph LearningCode1
DeeperGCN: All You Need to Train Deeper GCNsCode0
Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction—0
GEOM: Energy-annotated molecular conformations for property prediction and molecular generationCode1
Optimal Transport Graph Neural NetworksCode1
Enforcing Predictive Invariance across Structured Biomedical Domains—0
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