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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 211–220 of 691 papers

TitleStatusHype
Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model—0
TRIDENT: Tri-Modal Molecular Representation Learning with Taxonomic Annotations and Local Correspondence—0
Large Language Model Agent for Modular Task Execution in Drug Discovery—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
GeoRecon: Graph-Level Representation Learning for 3D Molecules via Reconstruction-Based Pretraining—0
Information fusion strategy integrating pre-trained language model and contrastive learning for materials knowledge mining—0
Robust Molecular Property Prediction via Densifying Scarce Labeled DataCode0
Breaking Bad Molecules: Are MLLMs Ready for Structure-Level Molecular Detoxification?—0
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