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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 71–80 of 691 papers

TitleStatusHype
Can Large Language Models Understand Molecules?Code1
Molecular Hypergraph Neural NetworksCode1
Graph Transformers for Large GraphsCode1
3DReact: Geometric deep learning for chemical reactionsCode1
Higher-Order Equivariant Neural Networks for Charge Density Prediction in MaterialsCode1
Removing Biases from Molecular Representations via Information MaximizationCode1
Multimodal Foundation Models for Material Property Prediction and DiscoveryCode1
Improving Self-supervised Molecular Representation Learning using Persistent HomologyCode1
Multi-channel learning for integrating structural hierarchies into context-dependent molecular representationCode1
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
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