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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
A new perspective on building efficient and expressive 3D equivariant graph neural networksCode1
Improving Self-supervised Molecular Representation Learning using Persistent HomologyCode1
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property PredictionCode1
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text ModelingCode1
Fast Quantum Property Prediction via Deeper 2D and 3D Graph NetworksCode1
Chemical-Reaction-Aware Molecule Representation LearningCode1
InversionGNN: A Dual Path Network for Multi-Property Molecular OptimizationCode1
ChemLLM: A Chemical Large Language ModelCode1
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning TreesCode1
GPS++: Reviving the Art of Message Passing for Molecular Property PredictionCode1
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