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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 481–490 of 691 papers

TitleStatusHype
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model—0
Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?—0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
Tanimoto Random Features for Scalable Molecular Machine LearningCode0
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer GenerationCode0
Molecular geometric deep learningCode0
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction—0
Self-supervised Learning and Graph Classification under Heterophily—0
MUBen: Benchmarking the Uncertainty of Molecular Representation ModelsCode0
MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning—0
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