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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 411–420 of 691 papers

TitleStatusHype
Learning Topology-Specific Experts for Molecular Property PredictionCode1
Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks—0
Retrieved Sequence Augmentation for Protein Representation LearningCode1
Likelihood Annealing: Fast Calibrated Uncertainty for Regression—0
Graph Neural Networks Go Forward-Forward—0
An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning—0
GCI: A (G)raph (C)oncept (I)nterpretation FrameworkCode0
A Text-guided Protein Design FrameworkCode2
GPS++: Reviving the Art of Message Passing for Molecular Property PredictionCode1
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property PredictionCode0
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