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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
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
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
Generative Pre-Training from MoleculesCode1
Geometric Transformer for End-to-End Molecule Properties PredictionCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
Molecular Contrastive Learning of Representations via Graph Neural NetworksCode1
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
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
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property PredictionCode1
GPS++: Reviving the Art of Message Passing for Molecular Property PredictionCode1
Self-Supervised Graph Transformer on Large-Scale Molecular DataCode1
Graph Rationalization with Environment-based AugmentationsCode1
Pairwise Learning for Neural Link PredictionCode1
Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture SearchCode1
Graph Neural Networks Need Cluster-Normalize-Activate ModulesCode1
Bayesian Graph Neural Networks for Molecular Property PredictionCode1
Graph Transformers for Large GraphsCode1
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
Graph Self-supervised Learning with Accurate Discrepancy LearningCode1
HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity PredictionCode1
Heterogenous Ensemble of Models for Molecular Property PredictionCode1
Dual-view Molecule Pre-trainingCode1
Hierarchical Grammar-Induced Geometry for Data-Efficient 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
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise AttentionCode1
Fast Quantum Property Prediction via Deeper 2D and 3D Graph NetworksCode1
Molecular Contrastive Learning with Chemical Element Knowledge GraphCode1
Implicit Convolutional Kernels for Steerable CNNsCode1
MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property PredictionCode1
An algorithmic framework for synthetic cost-aware decision making in molecular designCode1
Molecule-Morphology Contrastive Pretraining for Transferable Molecular RepresentationCode1
E(n) Equivariant Topological Neural NetworksCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property PredictionCode1
Interactive Molecular Discovery with Natural LanguageCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text DescriptionsCode1
Known Unknowns: Out-of-Distribution Property Prediction in Materials and MoleculesCode1
InversionGNN: A Dual Path Network for Multi-Property Molecular OptimizationCode1
O-GNN: Incorporating Ring Priors into Molecular ModelingCode1
3DReact: Geometric deep learning for chemical reactionsCode1
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular PropertiesCode1
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