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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 201–210 of 691 papers

TitleStatusHype
Learning Large Graph Property Prediction via Graph Segment TrainingCode1
Learning Invariances in Neural NetworksCode1
A new perspective on building efficient and expressive 3D equivariant graph neural networksCode1
Learning Topology-Specific Experts for Molecular Property PredictionCode1
Learning to Split for Automatic Bias DetectionCode1
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised LearningCode1
LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property PredictionCode1
Contextualized Messages Boost Graph RepresentationsCode0
Molecular Graph Contrastive Learning with Line GraphCode0
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