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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

Showing 131140 of 691 papers

TitleStatusHype
TransPolymer: a Transformer-based language model for polymer property predictionsCode1
HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise AttentionCode1
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
FunQG: Molecular Representation Learning Via Quotient GraphsCode1
Unified 2D and 3D Pre-Training of Molecular RepresentationsCode1
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture SearchCode1
Graph-based Molecular Representation LearningCode1
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide ElectrocatalystsCode1
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