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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 2130 of 691 papers

TitleStatusHype
MHG-GNN: Combination of Molecular Hypergraph Grammar with Graph Neural NetworkCode2
Temporal Graph Benchmark for Machine Learning on Temporal GraphsCode2
SGFormer: Simplifying and Empowering Transformers for Large-Graph RepresentationsCode2
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language ModelsCode2
A Text-guided Protein Design FrameworkCode2
A Systematic Survey of Chemical Pre-trained ModelsCode2
Protein Representation Learning by Geometric Structure PretrainingCode2
Identity-aware Graph Neural NetworksCode2
Analyzing Learned Molecular Representations for Property PredictionCode2
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
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