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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 311–320 of 691 papers

TitleStatusHype
Multi-View Graph Neural Networks for Molecular Property Prediction—0
DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules—0
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models—0
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility—0
Knowledge-aware contrastive heterogeneous molecular graph learning—0
Knowledge-aware Contrastive Molecular Graph Learning—0
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup—0
Enforcing Predictive Invariance across Structured Biomedical Domains—0
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures—0
A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials—0
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