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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 441–450 of 691 papers

TitleStatusHype
Distribution Learning for Molecular Regression—0
Enforcing Predictive Invariance across Structured Biomedical Domains—0
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup—0
DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules—0
Multi-View Graph Neural Networks for Molecular Property Prediction—0
Dual-Modality Representation Learning for Molecular Property Prediction—0
Molecule Design by Latent Prompt Transformer—0
Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks—0
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