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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 551–575 of 691 papers

TitleStatusHype
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
Learning 3D Representations of Molecular Chirality with Invariance to Bond RotationsCode1
3D Infomax improves GNNs for Molecular Property PredictionCode1
Attentive Walk-Aggregating Graph Neural NetworksCode0
Motif-based Graph Self-Supervised Learning for Molecular Property PredictionCode1
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular GraphsCode1
A molecular hypergraph convolutional network with functional group information—0
3D Pre-training improves GNNs for Molecular Property Prediction—0
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond—0
G^3: Representation Learning and Generation for Geometric Graphs—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
Scalable deeper graph neural networks for high-performance materials property predictionCode1
Multi-task Learning with Domain Knowledge for Molecular Property Prediction—0
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
Optimal Decision Making in High-Throughput Virtual Screening Pipelines—0
Chemical-Reaction-Aware Molecule Representation LearningCode1
Prediction of properties of metal alloy materials based on machine learning—0
Multilingual Molecular Representation Learning via Contrastive Pre-training—0
Generative Pre-Training from MoleculesCode1
MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art—0
Latent Tree Decomposition Parsers for AMR-to-Text Generation—0
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction—0
Hierarchical graph neural nets can capture long-range interactionsCode1
Calibrated Uncertainty for Molecular Property Prediction using Ensembles of Message Passing Neural Networks—0
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property PredictionCode1
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