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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 2130 of 691 papers

TitleStatusHype
Recent Developments in GNNs for Drug Discovery0
GenIC: An LLM-Based Framework for Instance Completion in Knowledge GraphsCode0
Directed Graph Grammars for Sequence-based LearningCode1
Graph Positional Autoencoders as Self-supervised Learners0
Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph LanguagesCode1
Iterative Corpus Refinement for Materials Property Prediction Based on Scientific Texts0
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling0
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery0
MolTextNet: A Two-Million Molecule-Text Dataset for Multimodal Molecular Learning0
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