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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 391–400 of 691 papers

TitleStatusHype
Atom-Motif Contrastive Transformer for Molecular Property Prediction—0
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction—0
Automatic Identification of Chemical Moieties—0
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models—0
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction—0
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction—0
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing—0
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models—0
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