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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 211–220 of 691 papers

TitleStatusHype
Conformal Drug Property Prediction with Density Estimation under Covariate Shift—0
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering—0
Complete Neural Networks for Complete Euclidean Graphs—0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
Artificial Intelligence Enabled Material Behavior Prediction—0
ADMEOOD: Out-of-Distribution Benchmark for Drug Property Prediction—0
Material Property Prediction using Graphs based on Generically Complete Isometry Invariants—0
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model—0
AdaMR: Adaptable Molecular Representation for Unified Pre-training Strategy—0
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices—0
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