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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 351–360 of 691 papers

TitleStatusHype
Cross-Modal Learning for Chemistry Property Prediction: Large Language Models Meet Graph Machine Learning—0
PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal Material Property Prediction—0
Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design—0
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches—0
Out-of-distribution materials property prediction using adversarial learning based fine-tuning—0
LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library—0
SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction—0
Advancing Molecular Machine Learning Representations with Stereoelectronics-Infused Molecular Graphs—0
Question Rephrasing for Quantifying Uncertainty in Large Language Models: Applications in Molecular Chemistry Tasks—0
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