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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 641–650 of 691 papers

TitleStatusHype
Path-aware Siamese Graph Neural Network for Link PredictionCode0
DeeperGCN: All You Need to Train Deeper GCNsCode0
Graph Neural Networks for Carbon Dioxide Adsorption Prediction in Aluminium-Exchanged ZeolitesCode0
Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property PredictionCode0
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
DYMAG: Rethinking Message Passing Using Dynamical-systems-based WaveformsCode0
Transformers for molecular property prediction: Lessons learned from the past five yearsCode0
Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property PredictionCode0
Transformers for molecular property prediction: Domain adaptation efficiently improves performanceCode0
Speak It Out: Solving Symbol-Related Problems with Symbol-to-Language Conversion for Language ModelsCode0
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