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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 401–410 of 691 papers

TitleStatusHype
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining—0
Infusing Linguistic Knowledge of SMILES into Chemical Language Models—0
Integrating Chemical Language and Molecular Graph in Multimodal Fused Deep Learning for Drug Property Prediction—0
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example—0
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties—0
Invariance-Aware Randomized Smoothing Certificates—0
Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction—0
Is Self-Supervised Pretraining Good for Extrapolation in Molecular Property Prediction?—0
Iterative Corpus Refinement for Materials Property Prediction Based on Scientific Texts—0
KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction—0
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