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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 561–570 of 691 papers

TitleStatusHype
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction—0
Affinity-Aware Graph Networks—0
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
3D Graph Contrastive Learning for Molecular Property Prediction—0
Embedding Graphs on Grassmann ManifoldCode0
Triangular Contrastive Learning on Molecular Graphs—0
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction—0
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
Partial Product Aware Machine Learning on DNA-Encoded Libraries—0
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction—0
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