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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 361–370 of 691 papers

TitleStatusHype
A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials—0
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures—0
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction—0
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material Property Prediction—0
Adaptive Invariance for Molecule Property Prediction—0
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery—0
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision—0
AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy—0
ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction—0
Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches—0
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