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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 361–370 of 691 papers

TitleStatusHype
Temporal Graph Benchmark for Machine Learning on Temporal GraphsCode2
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model—0
Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?—0
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
Tanimoto Random Features for Scalable Molecular Machine LearningCode0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer GenerationCode0
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction—0
Molecular geometric deep learningCode0
Interactive Molecular Discovery with Natural LanguageCode1
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