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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 531–540 of 691 papers

TitleStatusHype
BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular RepresentationCode0
Molecular Joint Representation Learning via Multi-modal Information—0
An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022—0
Supervised Pretraining for Molecular Force Fields and Properties Prediction—0
Equivariant Networks for Crystal Structures—0
Isotropic Gaussian Processes on Finite Spaces of GraphsCode0
A 3D-Shape Similarity-based Contrastive Approach to Molecular Representation Learning—0
Leveraging Orbital Information and Atomic Feature in Deep Learning Model—0
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties—0
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property PredictionCode0
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