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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 451–460 of 691 papers

TitleStatusHype
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets—0
Towards Unified AI Drug Discovery with Multiple Knowledge Modalities—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Enhancing material property prediction with ensemble deep graph convolutional networks—0
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction—0
Equilibrium Aggregation: Encoding Sets via Optimization—0
Equivariant Graph Attention Networks for Molecular Property Prediction—0
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