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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
Graph neural networks for the prediction of molecular structure-property relationships—0
FunQG: Molecular Representation Learning Via Quotient GraphsCode1
Unified 2D and 3D Pre-Training of Molecular RepresentationsCode1
Uncertainty quantification for predictions of atomistic neural networksCode0
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture SearchCode1
Graph-based Molecular Representation LearningCode1
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction—0
Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data—0
Affinity-Aware Graph Networks—0
LIMO: Latent Inceptionism for Targeted Molecule GenerationCode1
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