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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 101110 of 691 papers

TitleStatusHype
Interactive Molecular Discovery with Natural LanguageCode1
Automated 3D Pre-Training for Molecular Property PredictionCode1
CrysMMNet: Multimodal Representation for Crystal Property PredictionCode1
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot LearningCode1
Learning Large Graph Property Prediction via Graph Segment TrainingCode1
O-GNN: Incorporating Ring Priors into Molecular ModelingCode1
Molecule-Morphology Contrastive Pretraining for Transferable Molecular RepresentationCode1
SELFormer: Molecular Representation Learning via SELFIES Language ModelsCode1
A new perspective on building efficient and expressive 3D equivariant graph neural networksCode1
Learning Harmonic Molecular Representations on Riemannian ManifoldCode1
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