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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 491–500 of 691 papers

TitleStatusHype
Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction—0
Graph Convolution: A High-Order and Adaptive Approach—0
Graph Convolutional Neural Networks for Polymers Property Prediction—0
Graph Generative Pre-trained Transformer—0
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications—0
Graph-level Protein Representation Learning by Structure Knowledge Refinement—0
Graph Multi-Similarity Learning for Molecular Property Prediction—0
Graph Networks with Spectral Message Passing—0
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