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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 31–40 of 691 papers

TitleStatusHype
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph LanguagesCode1
Directed Graph Grammars for Sequence-based LearningCode1
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
Wyckoff Transformer: Generation of Symmetric CrystalsCode1
InversionGNN: A Dual Path Network for Multi-Property Molecular OptimizationCode1
Known Unknowns: Out-of-Distribution Property Prediction in Materials and MoleculesCode1
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
Predictive Modeling and Uncertainty Quantification of Fatigue Life in Metal Alloys using Machine LearningCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language ModelsCode1
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