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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 521–530 of 691 papers

TitleStatusHype
Equivariant Graph Attention Networks for Molecular Property Prediction—0
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer—0
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug DiscoveryCode3
Improving Molecular Representation Learning with Metric Learning-enhanced Optimal TransportCode1
Graph Self-supervised Learning with Accurate Discrepancy LearningCode1
Regression Transformer: Concurrent sequence regression and generation for molecular language modelingCode1
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative GamesCode1
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN)—0
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events—0
Formula graph self-attention network for representation-domain independent materials discoveryCode0
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