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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 331–340 of 691 papers

TitleStatusHype
In-Context Learning for Few-Shot Molecular Property Prediction—0
Discriminative protein sequence modelling with Latent Space Diffusion—0
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining—0
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
Directional Message Passing on Molecular Graphs via Synthetic Coordinates—0
All You Need Is Synthetic Task Augmentation—0
A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials—0
Learning Metal Microstructural Heterogeneity through Spatial Mapping of Diffraction Latent Space Features—0
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example—0
Improving Performance Prediction of Electrolyte Formulations with Transformer-based Molecular Representation Model—0
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