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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

Showing 611620 of 691 papers

TitleStatusHype
Towards Data-Efficient Pretraining for Atomic Property PredictionCode0
Isotropic Gaussian Processes on Finite Spaces of GraphsCode0
Towards deep generation of guided wave representations for composite materialsCode0
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation LearningCode0
Interpreting the Syntactic and Social Elements of the Tweet Representations via Elementary Property Prediction TasksCode0
MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure PredictionCode0
Interpretation of Semantic Tweet RepresentationsCode0
Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE ModelCode0
Integrating convolutional layers and biformer network with forward-forward and backpropagation trainingCode0
Do Graph Neural Networks Work for High Entropy Alloys?Code0
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