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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 511–520 of 691 papers

TitleStatusHype
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and ReconstructionCode0
Deep Learning Methods for Small Molecule Drug Discovery: A Survey—0
Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks—0
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
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property PredictionCode0
Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction—0
Equivariant Message Passing Neural Network for Crystal Material DiscoveryCode0
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