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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 381–390 of 691 papers

TitleStatusHype
Graph Neural Networks for Molecules—0
Graph neural networks for the prediction of molecular structure-property relationships—0
Graph Neural Networks Go Forward-Forward—0
Graph Neural Networks in Modern AI-aided Drug Discovery—0
Graph Positional Autoencoders as Self-supervised Learners—0
Graph Residual based Method for Molecular Property Prediction—0
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters—0
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events—0
Guided Latent Slot Diffusion for Object-Centric Learning—0
HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations—0
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