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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 351–375 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
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