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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 471–480 of 691 papers

TitleStatusHype
Prediction of properties of metal alloy materials based on machine learning—0
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN)—0
Pre-training of Molecular GNNs via Conditional Boltzmann Generator—0
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction—0
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction—0
ProGAE: A Geometric Autoencoder-based Generative Model for Disentangling Protein Dynamics—0
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction—0
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction—0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
Pure Component Property Estimation Framework Using Explainable Machine Learning Methods—0
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