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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 301310 of 691 papers

TitleStatusHype
Data-Efficient Molecular Generation with Hierarchical Textual InversionCode0
Data-Driven Self-Supervised Graph Representation LearningCode0
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptorsCode0
Learning Hierarchical Interaction for Accurate 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
Large-Scale Knowledge Integration for Enhanced Molecular Property PredictionCode0
Isotropic Gaussian Processes on Finite Spaces of GraphsCode0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
Interpretation of Semantic Tweet RepresentationsCode0
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