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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 281–290 of 691 papers

TitleStatusHype
LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material Discovery—0
Equilibrium Aggregation: Encoding Sets via Optimization—0
CAST: Cross Attention based multimodal fusion of Structure and Text for materials property prediction—0
Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction—0
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
Can Molecular Evolution Mechanism Enhance Molecular Representation?—0
3D Molecular Geometry Analysis with 2D Graphs—0
Analysis of Atomistic Representations Using Weighted Skip-Connections—0
GNN-SKAN: Harnessing the Power of SwallowKAN to Advance Molecular Representation Learning with GNNs—0
Enhancing material property prediction with ensemble deep graph convolutional networks—0
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