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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 441–450 of 691 papers

TitleStatusHype
Heterogenous Ensemble of Models for Molecular Property PredictionCode1
Bidirectional Generation of Structure and Properties Through a Single Molecular Foundation ModelCode1
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property PredictionCode1
Equivariant Networks for Crystal Structures—0
ParticleGrid: Enabling Deep Learning using 3D Representation of MaterialsCode1
Materials Property Prediction with Uncertainty Quantification: A Benchmark StudyCode1
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Isotropic Gaussian Processes on Finite Spaces of GraphsCode0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
A Systematic Survey of Chemical Pre-trained ModelsCode2
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