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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
Explaining Deep Graph Networks with Molecular CounterfactualsCode1
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
AugLiChem: Data Augmentation Library of Chemical Structures for Machine LearningCode1
MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property PredictionCode1
GeomGCL: Geometric Graph Contrastive Learning for Molecular Property PredictionCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
Multiset-Equivariant Set Prediction with Approximate Implicit DifferentiationCode1
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life ScienceCode1
3D Infomax improves GNNs for Molecular Property PredictionCode1
Automated 3D Pre-Training for Molecular Property PredictionCode1
Directed Graph Grammars for Sequence-based LearningCode1
AutoMat: Enabling Automated Crystal Structure Reconstruction from Microscopy via Agentic Tool UseCode1
Global Concept Explanations for Graphs by Contrastive LearningCode1
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property PredictionCode1
GPS++: Reviving the Art of Message Passing for Molecular Property PredictionCode1
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture SearchCode1
Graph Transformers for Large GraphsCode1
Graph Neural Networks Need Cluster-Normalize-Activate ModulesCode1
Optimal Transport Graph Neural NetworksCode1
Bayesian Graph Neural Networks for Molecular Property PredictionCode1
ParticleGrid: Enabling Deep Learning using 3D Representation of MaterialsCode1
Graph Rationalization with Environment-based AugmentationsCode1
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
Self-Supervised Graph Transformer on Large-Scale Molecular DataCode1
HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity PredictionCode1
Dual-view Molecule Pre-trainingCode1
Heterogenous Ensemble of Models for Molecular Property PredictionCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural LanguageCode1
Higher-Order Equivariant Neural Networks for Charge Density Prediction in MaterialsCode1
Improving Molecular Representation Learning with Metric Learning-enhanced Optimal TransportCode1
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
Implicit Convolutional Kernels for Steerable CNNsCode1
Property Enhanced Instruction Tuning for Multi-task Molecule Generation with Large Language ModelsCode1
Fast Quantum Property Prediction via Deeper 2D and 3D Graph NetworksCode1
Molecular Contrastive Learning of Representations via Graph Neural NetworksCode1
E(n) Equivariant Topological Neural NetworksCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular GraphsCode1
Known Unknowns: Out-of-Distribution Property Prediction in Materials and MoleculesCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
Meta-Learning with Fewer Tasks through Task InterpolationCode1
Neural Message Passing for Quantum ChemistryCode1
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular PropertiesCode1
Retrieved Sequence Augmentation for Protein Representation LearningCode1
3DReact: Geometric deep learning for chemical reactionsCode1
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property PredictionCode1
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