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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 651–660 of 691 papers

TitleStatusHype
Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profilesCode0
Explanatory Masks for Neural Network Interpretability—0
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction—0
Towards Interpretable Molecular Graph Representation Learning—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
All SMILES Variational Autoencoder for Molecular Property Prediction and Optimization—0
Site-specific graph neural network for predicting protonation energy of oxygenate molecules—0
Gated Graph Recursive Neural Networks for Molecular Property Prediction—0
Molecule Property Prediction Based on Spatial Graph EmbeddingCode0
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information MaximizationCode1
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