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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 91100 of 691 papers

TitleStatusHype
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot LearningCode1
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
CACTUS: Chemistry Agent Connecting Tool-Usage to ScienceCode1
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life ScienceCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
Graph Neural Networks Need Cluster-Normalize-Activate ModulesCode1
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
3DReact: Geometric deep learning for chemical reactionsCode1
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