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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 381–390 of 691 papers

TitleStatusHype
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot LearningCode1
Set-based Neural Network Encoding Without Weight Tying—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Learning Large Graph Property Prediction via Graph Segment TrainingCode1
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
MolXPT: Wrapping Molecules with Text for Generative Pre-trainingCode0
Evaluating the roughness of structure-property relationships using pretrained molecular representations—0
A Kriging-Random Forest Hybrid Model for Real-time Ground Property Prediction during Earth Pressure Balance Shield Tunneling—0
Knowledge graph-enhanced molecular contrastive learning with functional prompt—0
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