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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 426–450 of 691 papers

TitleStatusHype
CrystalFramer: Rethinking the Role of Frames for SE(3)-Invariant Crystal Structure Modeling—0
Crystal Twins: Self-supervised Learning for Crystalline Material Property Prediction—0
Current Methods for Drug Property Prediction in the Real World—0
CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representations—0
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction—0
Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems—0
Deep Learning based Dimple Segmentation for Quantitative Fractography—0
Deep Learning for Computational Chemistry—0
Deep Learning Methods for Small Molecule Drug Discovery: A Survey—0
Deep Robust Subjective Visual Property Prediction in Crowdsourcing—0
A General Approach for Determining Applicability Domain of Machine Learning Models—0
Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing—0
Directed Graph Attention Neural Network Utilizing 3D Coordinates for Molecular Property Prediction—0
Directional Message Passing on Molecular Graphs via Synthetic Coordinates—0
Discriminative protein sequence modelling with Latent Space Diffusion—0
Distribution Learning for Molecular Regression—0
Enforcing Predictive Invariance across Structured Biomedical Domains—0
DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup—0
DualEquiNet: A Dual-Space Hierarchical Equivariant Network for Large Biomolecules—0
Multi-View Graph Neural Networks for Molecular Property Prediction—0
Dual-Modality Representation Learning for Molecular Property Prediction—0
Molecule Design by Latent Prompt Transformer—0
Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks—0
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