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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 301–310 of 691 papers

TitleStatusHype
Invariance-Aware Randomized Smoothing Certificates—0
BioLangFusion: Multimodal Fusion of DNA, mRNA, and Protein Language Models—0
Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks—0
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction—0
Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure—0
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction—0
Molecule Design by Latent Prompt Transformer—0
Dual-Modality Representation Learning for Molecular Property Prediction—0
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