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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 231–240 of 691 papers

TitleStatusHype
MolPLA: A Molecular Pretraining Framework for Learning Cores, R-Groups and their Linker JointsCode0
MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding AnalysisCode0
From Abstract to Actionable: Pairwise Shapley Values for Explainable AICode0
Molecular Property Prediction: A Multilevel Quantum Interactions Modeling PerspectiveCode0
Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property PredictionCode0
An open unified deep graph learning framework for discovering drug leadsCode0
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Molecular geometric deep learningCode0
Molecular Graph Contrastive Learning with Line GraphCode0
Molecular Graph Representation Learning via Structural Similarity InformationCode0
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