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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 231–240 of 691 papers

TitleStatusHype
Hybrid Quantum Graph Neural Network for Molecular Property Prediction—0
Data-Efficient Molecular Generation with Hierarchical Textual InversionCode0
Contrastive Dual-Interaction Graph Neural Network for Molecular Property PredictionCode1
CACTUS: Chemistry Agent Connecting Tool-Usage to ScienceCode1
The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMACode0
Global Concept Explanations for Graphs by Contrastive LearningCode1
ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey beesCode0
Kermut: Composite kernel regression for protein variant effectsCode1
Transformers for molecular property prediction: Lessons learned from the past five yearsCode0
HeMeNet: Heterogeneous Multichannel Equivariant Network for Protein Multitask Learning—0
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