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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 411–420 of 691 papers

TitleStatusHype
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
Chemical Property Prediction Under Experimental Biases—0
Chemi-net: a graph convolutional network for accurate drug property prediction—0
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction—0
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model—0
Material Property Prediction using Graphs based on Generically Complete Isometry Invariants—0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
Complete Neural Networks for Complete Euclidean Graphs—0
Conformal Drug Property Prediction with Density Estimation under Covariate Shift—0
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