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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 641–650 of 691 papers

TitleStatusHype
Set-based Meta-Interpolation for Few-Task Meta-Learning—0
Set-based Neural Network Encoding Without Weight Tying—0
Sheaf HyperNetworks for Personalized Federated Learning—0
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond—0
Simplicial Message Passing for Chemical Property Prediction—0
Site-specific graph neural network for predicting protonation energy of oxygenate molecules—0
Sliced Denoising: A Physics-Informed Molecular Pre-Training Method—0
SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction—0
Smirk: An Atomically Complete Tokenizer for Molecular Foundation Models—0
Soft causal learning for generalized molecule property prediction: An environment perspective—0
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