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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 351–360 of 691 papers

TitleStatusHype
Current Methods for Drug Property Prediction in the Real World—0
CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representations—0
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture SearchCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networksCode1
MD-HIT: Machine learning for materials property prediction with dataset redundancy controlCode1
Structural Property Prediction—0
ChiENN: Embracing Molecular Chirality with Graph Neural NetworksCode1
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