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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 591–600 of 691 papers

TitleStatusHype
Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?Code0
MolMetaLM: a Physicochemical Knowledge-Guided Molecular Meta Language ModelCode0
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker JointsCode0
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and ReconstructionCode0
MolXPT: Wrapping Molecules with Text for Generative Pre-trainingCode0
Rethinking Gradient-Based Methods: Multi-Property Materials Design Beyond Differentiable TargetsCode0
Motif-aware Attribute Masking for Molecular Graph Pre-trainingCode0
Learning Hierarchical Interaction for Accurate Molecular Property PredictionCode0
MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property PredictionCode0
MUBen: Benchmarking the Uncertainty of Molecular Representation ModelsCode0
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