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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

Showing 1–10 of 691 papers

TitleStatusHype
Heat Kernel Goes Topological—0
Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures—0
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model—0
Large Language Model Agent for Modular Task Execution in Drug Discovery—0
TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence—0
A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools—0
Pix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation—0
CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation LearningCode0
Descriptor-based Foundation Models for Molecular Property PredictionCode2
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
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