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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 401–425 of 691 papers

TitleStatusHype
Learning Harmonic Molecular Representations on Riemannian ManifoldCode1
HD-Bind: Encoding of Molecular Structure with Low Precision, Hyperdimensional Binary Representations—0
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
QUBO-inspired Molecular Fingerprint for Chemical Property Prediction—0
Geometric Deep Learning for Molecular Crystal Structure Prediction—0
Highly Accurate Quantum Chemical Property Prediction with Uni-Mol+Code3
Molecular Property Prediction by Semantic-invariant Contrastive Learning—0
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and ReconstructionCode0
Deep Learning Methods for Small Molecule Drug Discovery: A Survey—0
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
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
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