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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 141–150 of 691 papers

TitleStatusHype
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life ScienceCode1
GEOM: Energy-annotated molecular conformations for property prediction and molecular generationCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
E(n) Equivariant Topological Neural NetworksCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
A graph representation of molecular ensembles for polymer property predictionCode1
Learning Large Graph Property Prediction via Graph Segment TrainingCode1
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
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