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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 621–630 of 691 papers

TitleStatusHype
Multipath Graph Convolutional Neural NetworksCode0
Ranking Structured Objects with Graph Neural NetworksCode0
Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction—0
Knowledge-aware Contrastive Molecular Graph Learning—0
Molecular Representation Learning by Leveraging Chemical Information—0
Structure-Enhanced Meta-Learning For Few-Shot Graph ClassificationCode0
A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Dynamics—0
Graph Networks with Spectral Message Passing—0
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