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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 151–200 of 691 papers

TitleStatusHype
Improving Molecular Representation Learning with Metric Learning-enhanced Optimal TransportCode1
Graph Self-supervised Learning with Accurate Discrepancy LearningCode1
Regression Transformer: Concurrent sequence regression and generation for molecular language modelingCode1
GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative GamesCode1
Improving VAE based molecular representations for compound property predictionCode1
Pairwise Learning for Neural Link PredictionCode1
Molecular Contrastive Learning with Chemical Element Knowledge GraphCode1
AugLiChem: Data Augmentation Library of Chemical Structures for Machine LearningCode1
Multiset-Equivariant Set Prediction with Approximate Implicit DifferentiationCode1
Geometric Transformer for End-to-End Molecule Properties PredictionCode1
Why Propagate Alone? Parallel Use of Labels and Features on GraphsCode1
Relative Molecule Self-Attention TransformerCode1
Learning 3D Representations of Molecular Chirality with Invariance to Bond RotationsCode1
3D Infomax improves GNNs for Molecular Property PredictionCode1
Motif-based Graph Self-Supervised Learning for Molecular Property PredictionCode1
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular GraphsCode1
Scalable deeper graph neural networks for high-performance materials property predictionCode1
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
Chemical-Reaction-Aware Molecule Representation LearningCode1
Generative Pre-Training from MoleculesCode1
Hierarchical graph neural nets can capture long-range interactionsCode1
Generalization and Robustness Implications in Object-Centric LearningCode1
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property PredictionCode1
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life ScienceCode1
Dual-view Molecule Pre-trainingCode1
Fast Quantum Property Prediction via Deeper 2D and 3D Graph NetworksCode1
Simple GNN Regularisation for 3D Molecular Property Prediction & BeyondCode1
Artificial Intelligence in Drug Discovery: Applications and TechniquesCode1
SpreadGNN: Serverless Multi-task Federated Learning for Graph Neural NetworksCode1
Materials Representation and Transfer Learning for Multi-Property PredictionCode1
Meta-Learning with Fewer Tasks through Task InterpolationCode1
MEG: Generating Molecular Counterfactual Explanations for Deep Graph NetworksCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
Assigning Confidence to Molecular Property PredictionCode1
Molecular Contrastive Learning of Representations via Graph Neural NetworksCode1
Few-Shot Graph Learning for Molecular Property PredictionCode1
Molecular machine learning with conformer ensemblesCode1
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug DiscoveryCode1
Bayesian Graph Neural Networks for Molecular Property PredictionCode1
Comparison of Atom Representations in Graph Neural Networks for Molecular Property PredictionCode1
Explaining Deep Graph Networks with Molecular CounterfactualsCode1
TrimNet: learning molecular representation from triplet messages for biomedicineCode1
Learning Invariances in Neural NetworksCode1
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property PredictionCode1
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular PropertiesCode1
A community-powered search of machine learning strategy space to find NMR property prediction modelsCode1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
Object-Centric Learning with Slot AttentionCode1
Self-Supervised Graph Transformer on Large-Scale Molecular DataCode1
Wasserstein Embedding for Graph LearningCode1
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