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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 101–150 of 691 papers

TitleStatusHype
A new perspective on building efficient and expressive 3D equivariant graph neural networksCode1
Global Attention based Graph Convolutional Neural Networks for Improved Materials Property PredictionCode1
ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property PredictionCode1
3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text ModelingCode1
Graph-based Molecular Representation LearningCode1
Chemical-Reaction-Aware Molecule Representation LearningCode1
Graph Neural Networks Need Cluster-Normalize-Activate ModulesCode1
ChemLLM: A Chemical Large Language ModelCode1
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning TreesCode1
ChiENN: Embracing Molecular Chirality with Graph Neural NetworksCode1
CHILI: Chemically-Informed Large-scale Inorganic Nanomaterials Dataset for Advancing Graph Machine LearningCode1
Graph Transformers for Large GraphsCode1
Molecule-Morphology Contrastive Pretraining for Transferable Molecular RepresentationCode1
3DReact: Geometric deep learning for chemical reactionsCode1
Learning Topology-Specific Experts for Molecular Property PredictionCode1
HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity PredictionCode1
Learning Harmonic Molecular Representations on Riemannian ManifoldCode1
Equivariance Everywhere All At Once: A Recipe for Graph Foundation ModelsCode1
Learning Invariances in Neural NetworksCode1
Comparison of Atom Representations in Graph Neural Networks for Molecular Property PredictionCode1
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property PredictionCode1
Heterogenous Ensemble of Models for Molecular Property PredictionCode1
Learning to Split for Automatic Bias DetectionCode1
Hierarchical graph neural nets can capture long-range interactionsCode1
Lo-Hi: Practical ML Drug Discovery BenchmarkCode1
Higher-Order Equivariant Neural Networks for Charge Density Prediction in MaterialsCode1
InversionGNN: A Dual Path Network for Multi-Property Molecular OptimizationCode1
Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property PredictionCode1
Known Unknowns: Out-of-Distribution Property Prediction in Materials and MoleculesCode1
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information MaximizationCode1
CrysMMNet: Multimodal Representation for Crystal Property PredictionCode1
Interactive Molecular Discovery with Natural LanguageCode1
KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property PredictionCode1
CrysGNN : Distilling pre-trained knowledge to enhance property prediction for crystalline materialsCode1
AstroCLIP: A Cross-Modal Foundation Model for GalaxiesCode1
Directed Graph Grammars for Sequence-based LearningCode1
Improving Self-supervised Molecular Representation Learning using Persistent HomologyCode1
Improving VAE based molecular representations for compound property predictionCode1
A Gaze into the Internal Logic of Graph Neural Networks, with LogicCode1
Dual-view Molecule Pre-trainingCode1
Assigning Confidence to Molecular Property PredictionCode1
Copolymer Informatics with Multi-Task Deep Neural NetworksCode1
DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life ScienceCode1
Data-Centric Learning from Unlabeled Graphs with Diffusion ModelCode1
Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human LanguageCode1
E(n) Equivariant Topological Neural NetworksCode1
DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and moleculesCode1
A graph representation of molecular ensembles for polymer property predictionCode1
Learning Large Graph Property Prediction via Graph Segment TrainingCode1
A Cartesian Encoding Graph Neural Network for Crystal Structures Property Prediction: Application to Thermal Ellipsoid EstimationCode1
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