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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 431–440 of 691 papers

TitleStatusHype
Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials -- A minireview—0
Machine learning for accelerating effective property prediction for poroelasticity problem in stochastic media—0
Molecular Property Prediction Based on Graph Structure Learning—0
Molecular Property Prediction by Semantic-invariant Contrastive Learning—0
Molecular Representation Learning by Leveraging Chemical Information—0
Molecular topological deep learning for polymer property prediction—0
Molecule Design by Latent Prompt Transformer—0
MoleHD: Ultra-Low-Cost Drug Discovery using Hyperdimensional Computing—0
MolGraph-xLSTM: A graph-based dual-level xLSTM framework with multi-head mixture-of-experts for enhanced molecular representation and interpretability—0
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction—0
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