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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 241–250 of 691 papers

TitleStatusHype
ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction—0
Gaussian Process Molecule Property Prediction with FlowMO—0
GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions—0
Extracting Material Property Measurement Data from Scientific Articles—0
Explanatory Masks for Neural Network Interpretability—0
Generate Novel Molecules With Target Properties Using Conditional Generative Models—0
Chemi-net: a graph convolutional network for accurate drug property prediction—0
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery—0
Adaptive Invariance for Molecule Property Prediction—0
Graph Neural Network for Hamiltonian-Based Material Property Prediction—0
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