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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 301–350 of 691 papers

TitleStatusHype
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
A Straightforward Gradient-Based Approach for High-Tc Superconductor Design: Leveraging Domain Knowledge via Adaptive Constraints—0
Efficient Training of Transformers for Molecule Property Prediction on Small-scale Datasets—0
Towards Unified AI Drug Discovery with Multiple Knowledge Modalities—0
Empowering Graph Representation Learning with Paired Training and Graph Co-Attention—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Enhancing material property prediction with ensemble deep graph convolutional networks—0
Ensemble Knowledge Distillation for Machine Learning Interatomic Potentials—0
Ensemble Model With Bert,Roberta and Xlnet For Molecular property prediction—0
Equilibrium Aggregation: Encoding Sets via Optimization—0
Equivariant Graph Attention Networks for Molecular Property Prediction—0
Equivariant Networks for Crystal Structures—0
Equivariant Neural Tangent Kernels—0
An Equivariant Pretrained Transformer for Unified 3D Molecular Representation Learning—0
Evaluating multiple models using labeled and unlabeled data—0
Evaluating Scalable Uncertainty Estimation Methods for DNN-Based Molecular Property Prediction—0
Evaluating the diversity and utility of materials proposed by generative models—0
Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions—0
Evaluating the roughness of structure-property relationships using pretrained molecular representations—0
EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations—0
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models—0
Explanatory Masks for Neural Network Interpretability—0
Extracting Material Property Measurement Data from Scientific Articles—0
Extracting Molecular Properties from Natural Language with Multimodal Contrastive Learning—0
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry—0
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs—0
FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction—0
FragmentNet: Adaptive Graph Fragmentation for Graph-to-Sequence Molecular Representation Learning—0
FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields—0
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