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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 421–430 of 691 papers

TitleStatusHype
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction—0
Equivariant Message Passing Neural Network for Crystal Material DiscoveryCode0
Complete Neural Networks for Complete Euclidean Graphs—0
Outlier-Based Domain of Applicability Identification for Materials Property Prediction ModelsCode0
CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materialsCode1
Reprogramming Pretrained Language Models for Protein Sequence Representation Learning—0
HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity PredictionCode1
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning—0
Material Property Prediction using Graphs based on Generically Complete Isometry Invariants—0
Pushing the boundaries of molecular property prediction for drug discovery with multitask learning BERT enhanced by SMILES enumerationCode1
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