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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 291300 of 691 papers

TitleStatusHype
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
DYMAG: Rethinking Message Passing Using Dynamical-systems-based WaveformsCode0
Data-Efficient Molecular Generation with Hierarchical Textual InversionCode0
Data-Driven Self-Supervised Graph Representation LearningCode0
Learning Hierarchical Interaction for Accurate Molecular Property PredictionCode0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
Graph Neural Networks for Surfactant Multi-Property PredictionCode0
Cuvis.Ai: An Open-Source, Low-Code Software Ecosystem for Hyperspectral Processing and ClassificationCode0
Interpreting the Syntactic and Social Elements of the Tweet Representations via Elementary Property Prediction TasksCode0
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation LearningCode0
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