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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 571–580 of 691 papers

TitleStatusHype
Transferring Chemical and Energetic Knowledge Between Molecular Systems with Machine Learning—0
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
Graph Anisotropic DiffusionCode0
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup—0
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications—0
Automatic Identification of Chemical Moieties—0
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility—0
Equilibrium Aggregation: Encoding Sets via Optimization—0
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