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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 581–590 of 691 papers

TitleStatusHype
MoMa: A Modular Deep Learning Framework for Material Property Prediction—0
Multi-modal cascade feature transfer for polymer property prediction—0
Multi-modal Contrastive Learning with Negative Sampling Calibration for Phenotypic Drug Discovery—0
MultiModal-Learning for Predicting Molecular Properties: A Framework Based on Image and Graph Structures—0
Multiparameter Persistent Homology for Molecular Property Prediction—0
Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data—0
Multi-task Learning with Domain Knowledge for Molecular Property Prediction—0
Neural Message Passing on High Order Paths—0
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations—0
On Data Imbalance in Molecular Property Prediction with Pre-training—0
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