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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 491–500 of 691 papers

TitleStatusHype
Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements—0
RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching—0
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels—0
Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances—0
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation—0
Scaffold Splits Overestimate Virtual Screening Performance—0
Scalable Multi-Task Transfer Learning for Molecular Property Prediction—0
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction—0
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction—0
Self-supervised Learning and Graph Classification under Heterophily—0
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