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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 91–100 of 691 papers

TitleStatusHype
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties PredictionCode1
PeptideBERT: A Language Model based on Transformers for Peptide Property PredictionCode1
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational GraphsCode1
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and TextCode1
Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture SearchCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networksCode1
MD-HIT: Machine learning for materials property prediction with dataset redundancy controlCode1
ChiENN: Embracing Molecular Chirality with Graph Neural NetworksCode1
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
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