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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 291–300 of 691 papers

TitleStatusHype
MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability—0
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features—0
Tensor Completion for Surrogate Modeling of Material Property Prediction—0
ReactEmbed: A Cross-Domain Framework for Protein-Molecule Representation Learning via Biochemical Reaction NetworksCode0
Can Molecular Evolution Mechanism Enhance Molecular Representation?—0
Evaluating multiple models using labeled and unlabeled data—0
Molecular Graph Contrastive Learning with Line GraphCode0
Dual-Modality Representation Learning for Molecular Property Prediction—0
Text to Band Gap: Pre-trained Language Models as Encoders for Semiconductor Band Gap PredictionCode0
Graph Generative Pre-trained Transformer—0
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