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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 511–520 of 691 papers

TitleStatusHype
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
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