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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

Showing 3140 of 691 papers

TitleStatusHype
CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materialsCode1
CrysMMNet: Multimodal Representation for Crystal Property PredictionCode1
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
Comparison of Atom Representations in Graph Neural Networks for Molecular Property PredictionCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
ChiENN: Embracing Molecular Chirality with Graph Neural NetworksCode1
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text ModelingCode1
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
Chemical-Reaction-Aware Molecule Representation LearningCode1
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property PredictionCode1
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