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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 521–530 of 691 papers

TitleStatusHype
Supervised Pretraining for Material Property Prediction—0
Supervised Pretraining for Molecular Force Fields and Properties Prediction—0
Symmetry-Informed Graph Neural Networks for Carbon Dioxide Isotherm and Adsorption Prediction in Aluminum-Substituted Zeolites—0
Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction—0
TapWeight: Reweighting Pretraining Objectives for Task-Adaptive Pretraining—0
Task Addition in Multi-Task Learning by Geometrical Alignment—0
Tensor Completion for Surrogate Modeling of Material Property Prediction—0
Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model—0
GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networks—0
Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey—0
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