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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 376–400 of 691 papers

TitleStatusHype
Graph-level Protein Representation Learning by Structure Knowledge Refinement—0
Graph Multi-Similarity Learning for Molecular Property Prediction—0
Graph Networks with Spectral Message Passing—0
Graph Neural Network Architecture Search for Molecular Property Prediction—0
Graph Neural Network for Hamiltonian-Based Material Property Prediction—0
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
Heat Kernel Goes Topological—0
HELM: Hierarchical Encoding for mRNA Language Modeling—0
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning—0
Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks—0
Hybrid Quantum Graph Neural Network for Molecular Property Prediction—0
Image-Like Graph Representations for Improved Molecular Property Prediction—0
Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification—0
Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model—0
In-Context Learning for Few-Shot Molecular Property Prediction—0
In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs—0
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