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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 571–580 of 691 papers

TitleStatusHype
Molecular Graph Representation Learning via Structural Similarity InformationCode0
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling PerspectiveCode0
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine LearningCode0
SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property predictionCode0
Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profilesCode0
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular SimulationCode0
Molecular Graph Contrastive Learning with Line GraphCode0
Molecular geometric deep learningCode0
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding AnalysisCode0
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