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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 351–400 of 691 papers

TitleStatusHype
Current Methods for Drug Property Prediction in the Real World—0
CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representations—0
Interpretable Ensemble Learning for Materials Property Prediction with Classical Interatomic Potentials: Carbon as an Example—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture SearchCode1
Can Large Language Models Empower Molecular Property Prediction?Code1
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networksCode1
MD-HIT: Machine learning for materials property prediction with dataset redundancy controlCode1
Structural Property Prediction—0
ChiENN: Embracing Molecular Chirality with Graph Neural NetworksCode1
Temporal Graph Benchmark for Machine Learning on Temporal GraphsCode2
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model—0
Why Deep Models Often cannot Beat Non-deep Counterparts on Molecular Property Prediction?—0
Graph Sampling-based Meta-Learning for Molecular Property PredictionCode1
Tanimoto Random Features for Scalable Molecular Machine LearningCode0
Accelerating Molecular Graph Neural Networks via Knowledge Distillation—0
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer GenerationCode0
Beyond Chemical Language: A Multimodal Approach to Enhance Molecular Property Prediction—0
Molecular geometric deep learningCode0
Interactive Molecular Discovery with Natural LanguageCode1
SGFormer: Simplifying and Empowering Transformers for Large-Graph RepresentationsCode2
MUBen: Benchmarking the Uncertainty of Molecular Representation ModelsCode0
Self-supervised Learning and Graph Classification under Heterophily—0
Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language ModelsCode2
MolCAP: Molecular Chemical reActivity pretraining and prompted-finetuning enhanced molecular representation learning—0
Automated 3D Pre-Training for Molecular Property PredictionCode1
Efficient Approximations of Complete Interatomic Potentials for Crystal Property PredictionCode0
Simplicial Message Passing for Chemical Property Prediction—0
CrysMMNet: Multimodal Representation for Crystal Property PredictionCode1
A Crystal-Specific Pre-Training Framework for Crystal Material Property Prediction—0
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations—0
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot LearningCode1
Set-based Neural Network Encoding Without Weight Tying—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Learning Large Graph Property Prediction via Graph Segment TrainingCode1
Chemellia: An Ecosystem for Atomistic Scientific Machine Learning—0
MolXPT: Wrapping Molecules with Text for Generative Pre-trainingCode0
Evaluating the roughness of structure-property relationships using pretrained molecular representations—0
A Kriging-Random Forest Hybrid Model for Real-time Ground Property Prediction during Earth Pressure Balance Shield Tunneling—0
Knowledge graph-enhanced molecular contrastive learning with functional prompt—0
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction—0
O-GNN: Incorporating Ring Priors into Molecular ModelingCode1
3D Molecular Geometry Analysis with 2D Graphs—0
Molecule-Morphology Contrastive Pretraining for Transferable Molecular RepresentationCode1
Uni-QSAR: an Auto-ML Tool for Molecular Property PredictionCode3
Towards Unified AI Drug Discovery with Multiple Knowledge Modalities—0
SELFormer: Molecular Representation Learning via SELFIES Language ModelsCode1
A new perspective on building efficient and expressive 3D equivariant graph neural networksCode1
Equivariant Parameter Sharing for Porous Crystalline MaterialsCode0
A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials—0
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