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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 176–200 of 691 papers

TitleStatusHype
Heterogenous Ensemble of Models for Molecular Property PredictionCode1
Dual-view Molecule Pre-trainingCode1
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property PredictionCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural LanguageCode1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise AttentionCode1
Fast Quantum Property Prediction via Deeper 2D and 3D Graph NetworksCode1
Molecular Contrastive Learning with Chemical Element Knowledge GraphCode1
Implicit Convolutional Kernels for Steerable CNNsCode1
MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property PredictionCode1
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
Molecule-Morphology Contrastive Pretraining for Transferable Molecular RepresentationCode1
E(n) Equivariant Topological Neural NetworksCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property PredictionCode1
Interactive Molecular Discovery with Natural LanguageCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text DescriptionsCode1
Known Unknowns: Out-of-Distribution Property Prediction in Materials and MoleculesCode1
InversionGNN: A Dual Path Network for Multi-Property Molecular OptimizationCode1
O-GNN: Incorporating Ring Priors into Molecular ModelingCode1
3DReact: Geometric deep learning for chemical reactionsCode1
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular PropertiesCode1
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