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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 561–570 of 691 papers

TitleStatusHype
MatWheel: Addressing Data Scarcity in Materials Science Through Synthetic Data—0
May the Force be with You: Unified Force-Centric Pre-Training for 3D Molecular Conformations—0
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling—0
MetaFAP: Meta-Learning for Frequency Agnostic Prediction of Metasurface Properties—0
Meta-Learning GNN Initializations for Low-Resource Molecular Property Prediction—0
MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning—0
Multilingual Molecular Representation Learning via Contrastive Pre-training—0
MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning—0
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning—0
Uncertainty quantification of molecular property prediction using Bayesian neural network models—0
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