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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 591–600 of 691 papers

TitleStatusHype
Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation LearningCode0
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing—0
Image-Like Graph Representations for Improved Molecular Property Prediction—0
Directional Message Passing on Molecular Graphs via Synthetic Coordinates—0
SPECTRe: Substructure Processing, Enumeration, and Comparison Tool Resource: An efficient tool to encode all substructures of molecules represented in SMILES—0
Extracting Material Property Measurement Data from Scientific Articles—0
Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks—0
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in scienceCode0
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
Attentive Walk-Aggregating Graph Neural NetworksCode0
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