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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 371–380 of 691 papers

TitleStatusHype
Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs—0
Affinity-Aware Graph Networks—0
A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery—0
A Kriging-Random Forest Hybrid Model for Real-time Ground Property Prediction during Earth Pressure Balance Shield Tunneling—0
AlloyBERT: Alloy Property Prediction with Large Language Models—0
All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization—0
All You Need Is Synthetic Task Augmentation—0
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility—0
A molecular hypergraph convolutional network with functional group information—0
A Multiscale Graph Convolutional Network Using Hierarchical Clustering—0
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