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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 601–610 of 691 papers

TitleStatusHype
Molecular Contrastive Learning of Representations via Graph Neural NetworksCode1
Few-Shot Graph Learning for Molecular Property PredictionCode1
A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction—0
Identity-aware Graph Neural NetworksCode2
ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Dynamics—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
Graph Networks with Spectral Message Passing—0
Molecular machine learning with conformer ensemblesCode1
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug DiscoveryCode1
Directed Graph Attention Neural Network Utilizing 3D Coordinates for Molecular Property Prediction—0
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