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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 471–480 of 691 papers

TitleStatusHype
TransPolymer: a Transformer-based language model for polymer property predictionsCode1
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular SimulationCode0
HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise AttentionCode1
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions—0
Path-aware Siamese Graph Neural Network for Link PredictionCode0
Semi-Supervised Junction Tree Variational Autoencoder for Molecular Property PredictionCode1
A Gaze into the Internal Logic of Graph Neural Networks, with LogicCode1
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction—0
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