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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 541–550 of 691 papers

TitleStatusHype
Multiset-Equivariant Set Prediction with Approximate Implicit DifferentiationCode1
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
Geometric Transformer for End-to-End Molecule Properties PredictionCode1
Surrogate- and invariance-boosted contrastive learning for data-scarce applications in scienceCode0
Why Propagate Alone? Parallel Use of Labels and Features on GraphsCode1
Relative Molecule Self-Attention TransformerCode1
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