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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
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative GamesCode1
E(n) Equivariant Topological Neural NetworksCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
GEOM: Energy-annotated molecular conformations for property prediction and molecular generationCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
A graph representation of molecular ensembles for polymer property predictionCode1
GIT-Mol: A Multi-modal Large Language Model for Molecular Science with Graph, Image, and TextCode1
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
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