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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 421–430 of 691 papers

TitleStatusHype
Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery—0
Continuous Representation of Molecules Using Graph Variational Autoencoder—0
Coordinating Cross-modal Distillation for Molecular Property Prediction—0
Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning—0
CrysAtom: Distributed Representation of Atoms for Crystal Property Prediction—0
CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling—0
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction—0
Current Methods for Drug Property Prediction in the Real World—0
CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representations—0
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction—0
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