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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 361–370 of 691 papers

TitleStatusHype
GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions—0
Generate Novel Molecules With Target Properties Using Conditional Generative Models—0
Generative Deep Learning Framework for Inverse Design of Fuels—0
Geometric Deep Learning for Molecular Crystal Structure Prediction—0
Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction—0
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining—0
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
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
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