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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 451–460 of 691 papers

TitleStatusHype
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties—0
MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property PredictionCode1
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property PredictionCode0
Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction ClassificationCode0
Structure-based drug design with geometric deep learning—0
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction—0
Substructure-Atom Cross Attention for Molecular Representation Learning—0
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property PredictionCode0
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
Improving Molecular Pretraining with Complementary FeaturizationsCode0
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