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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 576–600 of 691 papers

TitleStatusHype
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup—0
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications—0
Automatic Identification of Chemical Moieties—0
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility—0
Equilibrium Aggregation: Encoding Sets via Optimization—0
Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision—0
Equivariant Graph Attention Networks for Molecular Property Prediction—0
Knowledge-informed Molecular Learning: A Survey on Paradigm Transfer—0
Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN)—0
GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events—0
Formula graph self-attention network for representation-domain independent materials discoveryCode0
Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical Discovery with Chemical Accuracy—0
Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation—0
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation LearningCode0
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing—0
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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