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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 476–500 of 691 papers

TitleStatusHype
Current Methods for Drug Property Prediction in the Real World—0
CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representations—0
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
Structural Property Prediction—0
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model—0
Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?—0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
Tanimoto Random Features for Scalable Molecular Machine LearningCode0
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer GenerationCode0
Molecular geometric deep learningCode0
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction—0
Self-supervised Learning and Graph Classification under Heterophily—0
MUBen: Benchmarking the Uncertainty of Molecular Representation ModelsCode0
MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning—0
Efficient Approximations of Complete Interatomic Potentials for Crystal Property PredictionCode0
Simplicial Message Passing for Chemical Property Prediction—0
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
Set-based Neural Network Encoding Without Weight Tying—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
MolXPT: Wrapping Molecules with Text for Generative Pre-trainingCode0
Evaluating the roughness of structure-property relationships using pretrained molecular representations—0
A Kriging-Random Forest Hybrid Model for Real-time Ground Property Prediction during Earth Pressure Balance Shield Tunneling—0
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