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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 451–475 of 691 papers

TitleStatusHype
Exploring Data-Driven Chemical SMILES Tokenization Approaches to Identify Key Protein-Ligand Binding Moieties—0
MOFormer: Self-Supervised Transformer model for Metal-Organic Framework Property PredictionCode1
Visualizing the Obvious: A Concreteness-based Ensemble Model for Noun Property PredictionCode0
Multimodal Model with Text and Drug Embeddings for Adverse Drug Reaction ClassificationCode0
Structure-based drug design with geometric deep learning—0
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction—0
Substructure-Atom Cross Attention for Molecular Representation Learning—0
ProGReST: Prototypical Graph Regression Soft Trees for Molecular Property PredictionCode0
OQM9HK: A Large-Scale Graph Dataset for Machine Learning in Materials ScienceCode1
Improving Molecular Pretraining with Complementary FeaturizationsCode0
polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informaticsCode1
Unraveling Key Elements Underlying Molecular Property Prediction: A Systematic StudyCode1
Periodic Graph Transformers for Crystal Material Property PredictionCode1
Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties—0
Artificial Intelligence in Material Engineering: A review on applications of AI in Material Engineering—0
Graph Neural Networks for Molecules—0
A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural LanguageCode1
SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients—0
Uni-Mol: A Universal 3D Molecular Representation Learning Framework—0
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters—0
TransPolymer: a Transformer-based language model for polymer property predictionsCode1
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular SimulationCode0
HiGNN: Hierarchical Informative Graph Neural Networks for Molecular Property Prediction Equipped with Feature-Wise AttentionCode1
Cloud-Based Real-Time Molecular Screening Platform with MolFormer—0
GEM-2: Next Generation Molecular Property Prediction Network by Modeling Full-range Many-body Interactions—0
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