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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 191–200 of 691 papers

TitleStatusHype
Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances—0
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction—0
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning TreesCode1
Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?Code0
Token-Mol 1.0: Tokenized drug design with large language model—0
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction—0
Multi-Peptide: Multimodality Leveraged Language-Graph Learning of Peptide PropertiesCode0
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields—0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
T- Hop: A framework for studying the importance path information in molecular graphs for chemical property prediction—0
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