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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 291–300 of 691 papers

TitleStatusHype
Multimodal Foundation Models for Material Property Prediction and DiscoveryCode1
Improving Self-supervised Molecular Representation Learning using Persistent HomologyCode1
GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networks—0
MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures—0
Coarse-Grained Configurational Polymer Fingerprints for Property Prediction using Machine LearningCode0
Multiparameter Persistent Homology for Molecular Property Prediction—0
Unsupervised Musical Object Discovery from AudioCode0
Advancing Drug Discovery with Enhanced Chemical Understanding via Asymmetric Contrastive Multimodal LearningCode0
Identifying Semantic Component for Robust Molecular Property PredictionCode0
Object-Centric Learning with Slot Mixture ModuleCode0
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