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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 181–190 of 691 papers

TitleStatusHype
Heterogenous Ensemble of Models for Molecular Property PredictionCode1
Pairwise Learning for Neural Link PredictionCode1
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
Higher-Order Equivariant Neural Networks for Charge Density Prediction in MaterialsCode1
HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise AttentionCode1
polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informaticsCode1
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
E(n) Equivariant Topological Neural NetworksCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
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