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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 326–350 of 691 papers

TitleStatusHype
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
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