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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 101–125 of 691 papers

TitleStatusHype
A new perspective on building efficient and expressive 3D equivariant graph neural networksCode1
A graph representation of molecular ensembles for polymer property predictionCode1
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property PredictionCode1
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text ModelingCode1
Interactive Molecular Discovery with Natural LanguageCode1
Chemical-Reaction-Aware Molecule Representation LearningCode1
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property PredictionCode1
ChemLLM: A Chemical Large Language ModelCode1
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning TreesCode1
ChiENN: Embracing Molecular Chirality with Graph Neural NetworksCode1
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
E(n) Equivariant Topological Neural NetworksCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
Explaining Deep Graph Networks with Molecular CounterfactualsCode1
FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of InterpretabilityCode1
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text DescriptionsCode1
A Gaze into the Internal Logic of Graph Neural Networks, with LogicCode1
Comparison of Atom Representations in Graph Neural Networks for Molecular Property PredictionCode1
Directed Graph Grammars for Sequence-based LearningCode1
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties PredictionCode1
Artificial Intelligence in Drug Discovery: Applications and TechniquesCode1
MD-HIT: Machine learning for materials property prediction with dataset redundancy controlCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
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