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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 381–390 of 691 papers

TitleStatusHype
Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge—0
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding AnalysisCode0
Equivariant Neural Tangent Kernels—0
Topological Neural Networks go Persistent, Equivariant, and Continuous—0
In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs—0
Scaffold Splits Overestimate Virtual Screening Performance—0
Sheaf HyperNetworks for Personalized Federated Learning—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction—0
Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property PredictionCode0
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