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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 501–510 of 691 papers

TitleStatusHype
Knowledge graph-enhanced molecular contrastive learning with functional prompt—0
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction—0
3D Molecular Geometry Analysis with 2D Graphs—0
Towards Unified AI Drug Discovery with Multiple Knowledge Modalities—0
Equivariant Parameter Sharing for Porous Crystalline MaterialsCode0
A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials—0
HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations—0
Geometric Deep Learning for Molecular Crystal Structure Prediction—0
QUBO-inspired Molecular Fingerprint for Chemical Property Prediction—0
Molecular Property Prediction by Semantic-invariant Contrastive Learning—0
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