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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 91–100 of 691 papers

TitleStatusHype
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning—0
Tensor Completion for Surrogate Modeling of Material Property Prediction—0
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features—0
MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability—0
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction NetworksCode0
Molecular Fingerprints Are Strong Models for Peptide Function PredictionCode3
Can Molecular Evolution Mechanism Enhance Molecular Representation?—0
Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine LearningCode1
Evaluating multiple models using labeled and unlabeled data—0
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