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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 491500 of 691 papers

TitleStatusHype
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide ElectrocatalystsCode1
Graph Rationalization with Environment-based AugmentationsCode1
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property PredictionCode1
Shortest Path Networks for Graph Property PredictionCode1
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
3D Graph Contrastive Learning for Molecular Property Prediction0
Pre-training via Denoising for Molecular Property PredictionCode1
Embedding Graphs on Grassmann ManifoldCode0
Triangular Contrastive Learning on Molecular Graphs0
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction0
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