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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 111120 of 691 papers

TitleStatusHype
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
Generalization and Robustness Implications in Object-Centric LearningCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
LLaMo: Large Language Model-based Molecular Graph AssistantCode1
CrysMMNet: Multimodal Representation for Crystal Property PredictionCode1
Graph Transformers for Large GraphsCode1
A Gaze into the Internal Logic of Graph Neural Networks, with LogicCode1
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property PredictionCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
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