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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 4150 of 691 papers

TitleStatusHype
Bayesian Graph Neural Networks for Molecular Property PredictionCode1
ChemLLM: A Chemical Large Language ModelCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
E(n) Equivariant Topological Neural NetworksCode1
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
Automated 3D Pre-Training for Molecular Property PredictionCode1
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural LanguageCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
CACTUS: Chemistry Agent Connecting Tool-Usage to ScienceCode1
AugLiChem: Data Augmentation Library of Chemical Structures for Machine LearningCode1
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