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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 331–340 of 691 papers

TitleStatusHype
Language models in molecular discovery—0
Temporal graph models fail to capture global temporal dynamicsCode0
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction—0
DYMAG: Rethinking Message Passing Using Dynamical-systems-based WaveformsCode0
Motif-aware Attribute Masking for Molecular Graph Pre-trainingCode0
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation—0
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property PredictionCode1
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
Materials Informatics Transformer: A Language Model for Interpretable Materials Properties PredictionCode1
PeptideBERT: A Language Model based on Transformers for Peptide Property PredictionCode1
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