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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

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Showing 201–210 of 691 papers

TitleStatusHype
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property PredictionCode1
Learning to Split for Automatic Bias DetectionCode1
A new perspective on building efficient and expressive 3D equivariant graph neural networksCode1
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property PredictionCode1
FunQG: Molecular Representation Learning Via Quotient GraphsCode1
Relative Molecule Self-Attention TransformerCode1
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property PredictionCode1
Pairwise Learning for Neural Link PredictionCode1
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery—0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
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