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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 211–220 of 691 papers

TitleStatusHype
Equivariant Neural Tangent Kernels—0
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text ModelingCode1
MMPolymer: A Multimodal Multitask Pretraining Framework for Polymer Property PredictionCode1
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
TAGMol: Target-Aware Gradient-guided Molecule GenerationCode1
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
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