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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 376–400 of 691 papers

TitleStatusHype
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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