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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 161170 of 691 papers

TitleStatusHype
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
Directed Graph Grammars for Sequence-based LearningCode1
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
Improving VAE based molecular representations for compound property predictionCode1
Interactive Molecular Discovery with Natural LanguageCode1
InversionGNN: A Dual Path Network for Multi-Property Molecular OptimizationCode1
Learning Harmonic Molecular Representations on Riemannian ManifoldCode1
Learning Invariances in Neural NetworksCode1
Learning Molecular Representation in a CellCode1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
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