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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 191200 of 691 papers

TitleStatusHype
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
PepMNet: a hybrid deep learning model for predicting peptide properties using hierarchical graph representationsCode1
PeptideBERT: A Language Model based on Transformers for Peptide Property PredictionCode1
polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informaticsCode1
3DReact: Geometric deep learning for chemical reactionsCode1
Learning Invariances in Neural NetworksCode1
Optimal Transport Graph Neural NetworksCode1
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