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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 61–70 of 691 papers

TitleStatusHype
Chem42: a Family of chemical Language Models for Target-aware Ligand Generation—0
Lyra: An Efficient and Expressive Subquadratic Architecture for Modeling Biological Sequences—0
MetaFAP: Meta-Learning for Frequency Agnostic Prediction of Metasurface Properties—0
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery—0
A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discovery—0
Transformers for molecular property prediction: Domain adaptation efficiently improves performanceCode0
CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling—0
Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE ModelCode0
Wyckoff Transformer: Generation of Symmetric CrystalsCode1
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