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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 391–400 of 691 papers

TitleStatusHype
A General Approach for Determining Applicability Domain of Machine Learning Models—0
Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models—0
Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property PredictionCode0
PatchProt: Hydrophobic patch prediction using protein foundation modelsCode0
GLaD: Synergizing Molecular Graphs and Language Descriptors for Enhanced Power Conversion Efficiency Prediction in Organic Photovoltaic Devices—0
Hybrid Quantum Graph Neural Network for Molecular Property Prediction—0
Data-Efficient Molecular Generation with Hierarchical Textual InversionCode0
The Role of Model Architecture and Scale in Predicting Molecular Properties: Insights from Fine-Tuning RoBERTa, BART, and LLaMACode0
ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey beesCode0
Transformers for molecular property prediction: Lessons learned from the past five yearsCode0
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