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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

Showing 671680 of 691 papers

TitleStatusHype
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property PredictionCode0
Attentive Walk-Aggregating Graph Neural NetworksCode0
GCI: A (G)raph (C)oncept (I)nterpretation FrameworkCode0
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property PredictionCode0
From Tokens to Materials: Leveraging Language Models for Scientific DiscoveryCode0
From Abstract to Actionable: Pairwise Shapley Values for Explainable AICode0
Formula graph self-attention network for representation-domain independent materials discoveryCode0
Fluid Viscosity Prediction Leveraging Computer Vision and Robot InteractionCode0
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in scienceCode0
Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformationCode0
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