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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 31–40 of 691 papers

TitleStatusHype
All You Need Is Synthetic Task Augmentation—0
Pure Component Property Estimation Framework Using Explainable Machine Learning Methods—0
Quotient Complex Transformer (QCformer) for Perovskite Data Analysis—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
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models—0
SA-GAT-SR: Self-Adaptable Graph Attention Networks with Symbolic Regression for high-fidelity material property predictionCode0
MatMMFuse: Multi-Modal Fusion model for Material Property PredictionCode0
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