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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
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling—0
AdaptMol: Adaptive Fusion from Sequence String to Topological Structure for Few-shot Drug Discovery—0
MolTextNet: A Two-Million Molecule-Text Dataset for Multimodal Molecular Learning—0
All You Need Is Synthetic Task Augmentation—0
Quotient Complex Transformer (QCformer) for Perovskite Data Analysis—0
Pure Component Property Estimation Framework Using Explainable Machine Learning Methods—0
Impact of SMILES Notational Inconsistencies on Chemical Language Model PerformanceCode0
Soft causal learning for generalized molecule property prediction: An environment perspective—0
Multi-modal cascade feature transfer for polymer property prediction—0
34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery—0
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