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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 391400 of 691 papers

TitleStatusHype
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction0
O-GNN: Incorporating Ring Priors into Molecular ModelingCode1
3D Molecular Geometry Analysis with 2D Graphs0
Molecule-Morphology Contrastive Pretraining for Transferable Molecular RepresentationCode1
Uni-QSAR: an Auto-ML Tool for Molecular Property PredictionCode3
Towards Unified AI Drug Discovery with Multiple Knowledge Modalities0
SELFormer: Molecular Representation Learning via SELFIES Language ModelsCode1
A new perspective on building efficient and expressive 3D equivariant graph neural networksCode1
Equivariant Parameter Sharing for Porous Crystalline MaterialsCode0
A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials0
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